text:
@inproceedings{khrenova2018molekulyarnoe140481066,
 author = {Хренова, М. Г. and Цирельсон, В. Г.},
 booktitle = {МАТЕМАТИКА. КОМПЬЮТЕР. ОБРАЗОВАНИЕ. XXV международная конференция},
 language = {russian},
 pages = {83--83},
 publisher = {R&G Dynamics Москва. Ижевск Москва  Ижевск},
 series = {Тезисы},
 title = {Молекулярное моделирование механизма гидролиза антибиотиков металло-бета-лактамазами},
 volume = {25},
 year = {2018}
}

repository: polyakoviv/LCC

repo_name: polyakoviv/LCC

branch_name: refs/heads/main


text:
@article{fredericks_performance_2018,
	title = {Performance {Metrics} {Required} of {Next}-{Generation} {Batteries} to {Electrify} {Vertical} {Takeoff} and {Landing} ({VTOL}) {Aircraft}},
	volume = {3},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.8b02195},
	doi = {10.1021/acsenergylett.8b02195},
	language = {en},
	number = {12},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Fredericks, William L. and Sripad, Shashank and Bower, Geoffrey C. and Viswanathan, Venkatasubramanian},
	month = dec,
	year = {2018},
	pages = {2989--2994},
	file = {Fredericks et al. - 2018 - Performance Metrics Required of Next-Generation Ba.pdf:/Users/ssripad/Zotero/storage/DTDMQK9N/Fredericks et al. - 2018 - Performance Metrics Required of Next-Generation Ba.pdf:application/pdf},
}

@article{sripad_performance_2017,
	title = {Performance {Metrics} {Required} of {Next}-{Generation} {Batteries} to {Make} a {Practical} {Electric} {Semi} {Truck}},
	volume = {2},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.7b00432},
	doi = {10.1021/acsenergylett.7b00432},
	language = {en},
	number = {7},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Sripad, Shashank and Viswanathan, Venkatasubramanian},
	month = jul,
	year = {2017},
	pages = {1669--1673},
	file = {Full Text:/Users/ssripad/Zotero/storage/CI4LEE55/Sripad and Viswanathan - 2017 - Performance Metrics Required of Next-Generation Ba.pdf:application/pdf},
}

@article{guttenberg_evaluating_2017,
	title = {Evaluating the {Potential} of {Platooning} in {Lowering} the {Required} {Performance} {Metrics} of {Li}-{Ion} {Batteries} to {Enable} {Practical} {Electric} {Semi}-{Trucks}},
	volume = {2},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.7b01022},
	doi = {10.1021/acsenergylett.7b01022},
	language = {en},
	number = {11},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Guttenberg, Matthew and Sripad, Shashank and Viswanathan, Venkatasubramanian},
	month = nov,
	year = {2017},
	pages = {2642--2646},
	file = {Guttenberg et al. - 2017 - Evaluating the Potential of Platooning in Lowering.pdf:/Users/ssripad/Zotero/storage/4B8GEDZG/Guttenberg et al. - 2017 - Evaluating the Potential of Platooning in Lowering.pdf:application/pdf},
}

@article{bills_universal_2020,
	title = {Universal {Battery} {Performance} and {Degradation} {Model} for {Electric} {Aircraft}},
	url = {http://arxiv.org/abs/2008.01527},
	abstract = {Electric mobility application design requires accurate and fast prediction of Li-ion battery performance and life. An emerging area of mobility enabled by electrification is electric vertical takeoff and landing aircraft (eVTOL) for urban air mobility (UAM). Design process, techno-economic analysis and the operation of on-board systems depend on the fast and accurate prediction of Li-ion battery performance and life. These models must be accurate for the unique use-case of UAM requiring high power at takeoff and landing, thereby, causing significant rise in battery cell temperature. In this work, we generate a battery performance and thermal dataset specific to eVTOL use-cases and develop a fast and accurate performance and degradation model around that dataset. We use a machine-learning based physics-informed battery performance model to break the typically observed accuracy-computing cost trade-off. We fit the aging parameters for each cycle in a given cell's lifetime, and then model the evolution of those parameters using a new approach that combines traditional physics-based models, consisting of SEI film growth, charge loss, and Li Plating, along with a neural network in a universal ordinary differential equations (u-ODEs) framework. The new approach provides a unique way to combine the extrapolative capability of physics-based models with the representation power of neural networks.},
	urldate = {2020-08-17},
	journal = {arXiv:2008.01527 [physics]},
	author = {Bills, Alexander and Sripad, Shashank and Fredericks, William L. and Guttenberg, Matthew and Charles, Devin and Frank, Evan and Viswanathan, Venkatasubramanian},
	month = jul,
	year = {2020},
	note = {arXiv: 2008.01527},
	keywords = {Physics - Applied Physics, Computer Science - Machine Learning},
	file = {arXiv.org Snapshot:/Users/ssripad/Zotero/storage/UJY8LHH6/2008.html:text/html;arXiv Fulltext PDF:/Users/ssripad/Zotero/storage/T4GH3ILA/Bills et al. - 2020 - Universal Battery Performance and Degradation Mode.pdf:application/pdf},
}

@article{mohan_trade-offs_2020,
	title = {Trade-offs between automation and light vehicle electrification},
	issn = {2058-7546},
	url = {https://doi.org/10.1038/s41560-020-0644-3},
	doi = {10.1038/s41560-020-0644-3},
	abstract = {Weight, computing load, sensor load and possibly higher drag may increase the energy use of automated electric vehicles relative to human-driven electric vehicles, although this increase may be offset by smoother driving. Here, we use a vehicle dynamics model to evaluate the trade-off between automation and electric vehicle range and battery longevity. We find that automation will likely reduce electric vehicle range by 5–10\% for suburban driving and by 10–15\% for city driving. The effect on range is strongly influenced by sensor drag for suburban driving and computing loads for city driving. The impact of automation on battery longevity is negligible. While some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles will be gas–electric hybrids, our results suggest that this need not be the case if automakers can implement energy-efficient computing and aerodynamic sensor stacks.},
	journal = {Nature Energy},
	author = {Mohan, Aniruddh and Sripad, Shashank and Vaishnav, Parth and Viswanathan, Venkatasubramanian},
	month = jun,
	year = {2020},
	file = {Mohan et al. - 2020 - Trade-offs between automation and light vehicle el.pdf:/Users/ssripad/Zotero/storage/ZI49HU5T/Mohan et al. - 2020 - Trade-offs between automation and light vehicle el.pdf:application/pdf},
}

@article{mohan_automation_2020,
	title = {Automation is no barrier to light vehicle electrification},
	url = {http://arxiv.org/abs/1908.08920},
	abstract = {Weight, computational load, sensor load, and possibly higher drag may increase the energy use of automated electric vehicles (AEVs) relative to human-driven electric vehicles (EVs), although this increase may be offset by smoother driving. We use a vehicle dynamics model to show that automation is likely to impose a minor penalty on EV range and have negligible effect on battery longevity. As such, while some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles (AVs) will be gas-electric hybrids, we conclude that this need not be the case. We also find that drivers need to place only a modest value on the time saved by automation for its benefits to exceed direct costs.},
	urldate = {2020-05-28},
	journal = {arXiv:1908.08920 [cs]},
	author = {Mohan, Aniruddh and Sripad, Shashank and Vaishnav, Parth and Viswanathan, Venkatasubramanian},
	month = feb,
	year = {2020},
	note = {arXiv: 1908.08920},
	keywords = {Computer Science - Computers and Society},
	file = {arXiv.org Snapshot:/Users/ssripad/Zotero/storage/3UB6DQB2/1908.html:text/html;arXiv Fulltext PDF:/Users/ssripad/Zotero/storage/PD5AEIBI/Mohan et al. - 2020 - Automation is no barrier to light vehicle electrif.pdf:application/pdf},
}

@article{sripad_future_2019,
	title = {The {Future} of {Vehicle} {Electrification} in {India} {May} {Ride} on {Two} {Wheels}},
	volume = {4},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.9b02103},
	doi = {10.1021/acsenergylett.9b02103},
	language = {en},
	number = {11},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Sripad, Shashank and Mehta, Tarun and Srivastava, Anil and Viswanathan, Venkatasubramanian},
	month = nov,
	year = {2019},
	pages = {2691--2694},
	file = {Full Text:/Users/ssripad/Zotero/storage/C3DF43TT/Sripad et al. - 2019 - The Future of Vehicle Electrification in India May.pdf:application/pdf},
}

@article{bills_performance_2020,
	title = {Performance {Metrics} {Required} of {Next}-{Generation} {Batteries} to {Electrify} {Commercial} {Aircraft}},
	volume = {5},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.9b02574},
	doi = {10.1021/acsenergylett.9b02574},
	language = {en},
	number = {2},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Bills, Alexander and Sripad, Shashank and Fredericks, William Leif and Singh, Madalsa and Viswanathan, Venkatasubramanian},
	month = feb,
	year = {2020},
	pages = {663--668},
	file = {Full Text:/Users/ssripad/Zotero/storage/69A5LDQT/Bills et al. - 2020 - Performance Metrics Required of Next-Generation Ba.pdf:application/pdf},
}

@article{sripad_vulnerabilities_2019,
	title = {Vulnerabilities of {Electric} {Vehicle} {Battery} {Packs} to {Cyberattacks}},
	url = {http://arxiv.org/abs/1711.04822},
	abstract = {Electric Vehicles (EVs), like all modern vehicles, are entirely controlled by electronic devices embedded within networks that are exposed to the threat of cyberattacks. Cyber vulnerabilities are magnified with EVs due to unique risks associated with EV battery packs. Current batteries have well-known issues with specific energy, cost and fire-related safety risks. In this study, we develop a systematic framework to assess the impact of cyberattacks on EVs. While the current focus of automotive cyberattacks is on short-term physical safety, it is crucial to consider long-term cyberattacks that aim to cause financial losses through accrued impact, especially in the context of EVs. Faulty components of battery management systems such as a compromised voltage regulator could lead to cyberattacks that can overdischarge or overcharge the battery. Overdischarge could lead to failures such as internal shorts in the timescale of minutes through cyberattacks that compromise energy-intensive EV subsystems like auxiliary components. Attacks that overcharge the pack could shorten the lifetime of a new battery pack to less than a year. Further, such attacks also pose physical safety risks via the triggering of thermal (fire) events. Attacks on auxiliary components lead to battery drain, which could be up to 20\% of the state-of-charge per hour. Lastly, we develop a heuristic for the stealthiness of a cyberattack to augment traditional threat models. The methodology presented here will help in building the foundational principles of electric vehicle cybersecurity: a nascent but critical topic in the coming years.},
	urldate = {2020-05-28},
	journal = {arXiv:1711.04822 [cs]},
	author = {Sripad, Shashank and Kulandaivel, Sekar and Pande, Vikram and Sekar, Vyas and Viswanathan, Venkatasubramanian},
	month = sep,
	year = {2019},
	note = {arXiv: 1711.04822},
	keywords = {Computer Science - Cryptography and Security},
	file = {arXiv.org Snapshot:/Users/ssripad/Zotero/storage/HNSBKMWY/1711.html:text/html;arXiv Fulltext PDF:/Users/ssripad/Zotero/storage/PJLCLQAS/Sripad et al. - 2019 - Vulnerabilities of Electric Vehicle Battery Packs .pdf:application/pdf},
}

@article{sripad_quantifying_2019,
	title = {Quantifying the {Economic} {Case} for {Electric} {Semi}-{Trucks}},
	volume = {4},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.8b02146},
	doi = {10.1021/acsenergylett.8b02146},
	language = {en},
	number = {1},
	urldate = {2020-05-28},
	journal = {ACS Energy Lett.},
	author = {Sripad, Shashank and Viswanathan, Venkatasubramanian},
	month = jan,
	year = {2019},
	pages = {149--155},
	file = {Submitted Version:/Users/ssripad/Zotero/storage/C5J3KDPQ/Sripad and Viswanathan - 2019 - Quantifying the Economic Case for Electric Semi-Tr.pdf:application/pdf},
}

@article{sripad_evaluation_2017,
	title = {Evaluation of {Current}, {Future}, and {Beyond} {Li}-{Ion} {Batteries} for the {Electrification} of {Light} {Commercial} {Vehicles}: {Challenges} and {Opportunities}},
	volume = {164},
	issn = {0013-4651, 1945-7111},
	shorttitle = {Evaluation of {Current}, {Future}, and {Beyond} {Li}-{Ion} {Batteries} for the {Electrification} of {Light} {Commercial} {Vehicles}},
	url = {https://iopscience.iop.org/article/10.1149/2.0671711jes},
	doi = {10.1149/2.0671711jes},
	language = {en},
	number = {11},
	urldate = {2020-05-28},
	journal = {J. Electrochem. Soc.},
	author = {Sripad, Shashank and Viswanathan, Venkatasubramanian},
	year = {2017},
	pages = {E3635--E3646},
	file = {Submitted Version:/Users/ssripad/Zotero/storage/PXP75HYS/Sripad and Viswanathan - 2017 - Evaluation of Current, Future, and Beyond Li-Ion B.pdf:application/pdf},
}

@article{sripad_promise_2021,
	title = {The promise of energy-efficient battery-powered urban aircraft},
	url = {http://arxiv.org/abs/2106.09513},
	abstract = {Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers. We find that novel UAM aircraft consume between 130 Wh/passenger-mile up to {\textasciitilde}1,200 Wh/passenger-mile depending on the design and utilization, relative to an expected consumption of over 220 Wh/passenger-mi for terrestrial electric vehicles and 1,000 Wh/passenger-mile for combustion engine vehicles. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power-and-energy while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.},
	urldate = {2021-08-27},
	journal = {arXiv:2106.09513 [cs, eess]},
	author = {Sripad, Shashank and Viswanathan, Venkatasubramanian},
	month = jun,
	year = {2021},
	note = {arXiv: 2106.09513},
	keywords = {Electrical Engineering and Systems Science - Systems and Control},
	file = {arXiv Fulltext PDF:/Users/ssripad/Zotero/storage/QYIB23UW/Sripad and Viswanathan - 2021 - The promise of energy-efficient battery-powered ur.pdf:application/pdf;arXiv.org Snapshot:/Users/ssripad/Zotero/storage/IPNER5CS/2106.html:text/html},
}

@article{guttenberg_incepts_2021,
	title = {{INCEPTS}: {Software} for high-fidelity electric vehicle en route state of charge estimation, fleet analysis and charger deployment},
	volume = {7},
	issn = {25901168},
	shorttitle = {{INCEPTS}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2590116821000047},
	doi = {10.1016/j.etran.2021.100106},
	language = {en},
	urldate = {2021-08-27},
	journal = {eTransportation},
	author = {Guttenberg, Matthew and Sripad, Shashank and Bills, Alexander and Viswanathan, Venkatasubramanian},
	month = feb,
	year = {2021},
	pages = {100106},
	file = {Full Text:/Users/ssripad/Zotero/storage/9TPATE7K/Guttenberg et al. - 2021 - INCEPTS Software for high-fidelity electric vehic.pdf:application/pdf},
}

@article{sripad_kinetics_2020,
	title = {Kinetics of lithium electrodeposition and stripping},
	volume = {153},
	issn = {0021-9606, 1089-7690},
	url = {http://aip.scitation.org/doi/10.1063/5.0023771},
	doi = {10.1063/5.0023771},
	language = {en},
	number = {19},
	urldate = {2021-08-27},
	journal = {J. Chem. Phys.},
	author = {Sripad, Shashank and Korff, Daniel and DeCaluwe, Steven C. and Viswanathan, Venkatasubramanian},
	month = nov,
	year = {2020},
	pages = {194701},
	file = {Submitted Version:/Users/ssripad/Zotero/storage/DQ2VHM9E/Sripad et al. - 2020 - Kinetics of lithium electrodeposition and strippin.pdf:application/pdf},
}

@article{sripad_review_2021,
	title = {A review of safety considerations for batteries in aircraft with electric propulsion},
	volume = {46},
	issn = {0883-7694, 1938-1425},
	url = {https://link.springer.com/10.1557/s43577-021-00097-1},
	doi = {10.1557/s43577-021-00097-1},
	abstract = {Abstract
            Modern aircraft designs for “more electric” and “fully electric” aircraft have large battery packs ranging from tens of kWh for urban aviation to hundreds or thousands of kWh for commercial aviation. Such large battery packs require careful consideration of the safety concerns unique to aviation. The most pertinent safety concerns related to batteries can be categorized into two broad areas: exothermic heat related events (thermal issues) and partial or complete loss of safety–critical power supply (functional issues). Degradation during operation of a battery can contribute to capacity fade, increased internal resistance, power fade, and internal short circuits, which lead to the loss of or decrease in propulsive power. When batteries are the primary source of onboard power and energy, it is crucial to be able to estimate their state-of-health in terms of capacity and power capability. Internal short circuits and other sources of excessive heat generation can lead to high temperatures within the cells of a battery pack leading to safety concerns and thermal events. One of the biggest risk factors for batteries used in aviation is the potential for thermal runaway where temperatures reach the flashpoint of one of the cell components, eventually cascading over multiple cells leading to system-wide battery pack failure and a fire hazard. This article reviews the current understanding of the safety concerns related to batteries in the context of urban and regional electric aviation.},
	language = {en},
	number = {5},
	urldate = {2021-08-27},
	journal = {MRS Bulletin},
	author = {Sripad, Shashank and Bills, Alexander and Viswanathan, Venkatasubramanian},
	month = may,
	year = {2021},
	pages = {435--442},
	file = {Full Text:/Users/ssripad/Zotero/storage/XCVJ3SH5/Sripad et al. - 2021 - A review of safety considerations for batteries in.pdf:application/pdf},
}

@article{baker_fostering_2020,
	title = {Fostering a {Sustainable} {Community} in {Batteries}},
	volume = {5},
	issn = {2380-8195, 2380-8195},
	url = {https://pubs.acs.org/doi/10.1021/acsenergylett.0c01304},
	doi = {10.1021/acsenergylett.0c01304},
	language = {en},
	number = {7},
	urldate = {2021-08-27},
	journal = {ACS Energy Lett.},
	author = {Baker, Jenny A. and Beuse, Martin and DeCaluwe, Steven C. and Jing, Linda W. and Khoo, Edwin and Sripad, Shashank and Ulissi, Ulderico and Verma, Ankit and Wang, Andrew A. and Yeh, Yen T. and Yiu, Nicholas and Howey, David A. and Viswanathan, Venkatasubramanian},
	month = jul,
	year = {2020},
	pages = {2361--2366},
	file = {Full Text:/Users/ssripad/Zotero/storage/NZDREH77/Baker et al. - 2020 - Fostering a Sustainable Community in Batteries.pdf:application/pdf},
}

license: MIT

repository: ssripad/cv

license_type: permissive

repo_name: ssripad/cv

extension: bib


text:
@article{turing37,
  author = {Turing, A. M.},
  title = {On Computable Numbers, with an Application to the Entscheidungsproblem},
  volume = {s2-42},
  number = {1},
  pages = {230-265},
  year = {1937},
  doi = {10.1112/plms/s2-42.1.230},
  URL = {http://plms.oxfordjournals.org/content/s2-42/1/230.short},
  eprint = {http://plms.oxfordjournals.org/content/s2-42/1/230.full.pdf+html},
  journal = {Proceedings of the London Mathematical Society}
}

@book{biggs02,
  title={Discrete Mathematics},
  author={Biggs, N.},
  isbn={9780198507178},
  lccn={85008964},
  series={Oxford science publications},
  url={https://books.google.ie/books?id=Mj9gzZMrXDIC},
  year={2002},
  publisher={OUP Oxford}
}

@book{sipser2006,
  added-at = {2014-03-03T20:31:33.000+0100},
  author = {Sipser, Michael},
  biburl = {https://www.bibsonomy.org/bibtex/2317441ce12cacafa82cd32c4b2ed95cc/ytyoun},
  edition = {Second},
  interhash = {6d6f89523b99e25120368e673ca59c01},
  intrahash = {317441ce12cacafa82cd32c4b2ed95cc},
  isbn = {7111173279 9787111173274},
  keywords = {automata complexity computation gadget hamiltonian np-hardness sipser textbook},
  publisher = {Course Technology},
  refid = {660595094},
  timestamp = {2016-12-04T08:23:10.000+0100},
  title = {Introduction to the Theory of Computation},
  year = 2006
}

repository: theory-of-algorithms/problems-turing

repo_name: theory-of-algorithms/problems-turing

branch_name: refs/heads/master


text:
@book{Albert2009,
  address = {New York, NY},
  author = {Albert, Jim},
  doi = {10.1007/978-0-387-92298-0},
  isbn = {978-0-387-92297-3},
  mendeley-groups = {statistica},
  publisher = {Springer New York},
  title = {{Bayesian Computation with R}},
  url = {http://link.springer.com/10.1007/978-0-387-92298-0},
  year = {2009}
}
@article{Andrews2013,
  author = {Andrews, Mark and Baguley, Thom},
  doi = {10.1111/bmsp.12004},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/bmsp.12004.pdf:pdf},
  issn = {00071102},
  journal = {British Journal of Mathematical and Statistical Psychology},
  mendeley-groups = {statistica},
  month = {feb},
  number = {1},
  pages = {1--7},
  pmid = {23330865},
  title = {{Prior approval: The growth of Bayesian methods in psychology}},
  url = {http://doi.wiley.com/10.1111/bmsp.12004},
  volume = {66},
  year = {2013}
}
@article{Kruschke2012,
  abstract = {The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles published in 15 journals from January 2001 and December 2010, indicate that Bayesian approaches are essentially absent from the organizational sciences. Our article introduces organizational science researchers to Bayesian methods and describes why and how they should be used. We use multiple linear regression as the framework to offer a step-by-step demonstration, including the use of software, regarding how to implement Bayesian methods. We explain and illustrate how to determine the prior distribution, compute the posterior distribution, possibly accept the null value, and produce a write-up describing the entire Bayesian process, including graphs, results, and their interpretation. We also offer a summary of the advantages of using Bayesian analysis and examples of how specific published research based on frequentist analysis-based approaches failed to benefit from the advantages offered by a Bayesian approach and how using Bayesian analyses would have led to richer and, in some cases, different substantive conclusions. We hope that our article will serve as a catalyst for the adoption of Bayesian methods in organizational science research. {\textcopyright} The Author(s) 2012.},
  author = {Kruschke, John K. and Aguinis, Herman and Joo, Harry},
  doi = {10.1177/1094428112457829},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/1094428112457829.pdf:pdf},
  issn = {1094-4281},
  journal = {Organizational Research Methods},
  keywords = {Monte Carlo,bootstrapping),computer simulation procedures (e.g.,multilevel research,quantitative research},
  mendeley-groups = {statistica},
  month = {oct},
  number = {4},
  pages = {722--752},
  title = {{The Time Has Come: Bayesian Methods for Data Analysis in the Organizational Sciences }},
  url = {http://journals.sagepub.com/doi/10.1177/1094428112457829},
  volume = {15},
  year = {2012}
}
@article{Stefan2019,
  abstract = {The possibility that certain microorganisms might be beneficial to human health is highlighted by the numerous consumer products containing probiotic bacteria. Probiotics are typically administered in food that, following entry into the gastrointestinal tract, results in measurable health-promoting effects. Although there is a growing list of health benefits provided by the consumption of probiotics, their precise mechanisms of action remain largely unknown. Recent molecular- and genomics-based studies are starting to provide insight into the ways probiotic bacteria sense and adapt to the gastrointestinal tract environment. Complementary approaches using host cell in vitro systems together with animal models and human volunteers are revealing specific intestinal cell responses to probiotics. These studies should ultimately disclose the molecular mechanisms and pinpoint the bacterial and host effector molecules and pathways by which probiotics are able to modulate human health.},
  author = {Stefan, Angelika M. and Gronau, Quentin Frederik and Sch{\"{o}}nbrodt, Felix D. and Wagenmakers, Eric-Jan},
  doi = {10.3758/s13428-018-01189-8},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/Stefan2019_Article_ATutorialOnBayesFactorDesignAn.pdf:pdf},
  isbn = {1342801801189},
  issn = {1554-3528},
  journal = {Behavior Research Methods},
  keywords = {Bayes Factor,Design Analysis,Power Analysis,Sample Size,Statistical Evidence},
  mendeley-groups = {statistica},
  month = {jun},
  number = {3},
  pages = {1042--1058},
  title = {{A tutorial on Bayes Factor Design Analysis using an informed prior}},
  url = {http://link.springer.com/10.3758/s13428-018-01189-8},
  volume = {51},
  year = {2019}
}
@article{Etz2016,
  abstract = {We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors - a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis - for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.},
  author = {Etz, Alexander and Vandekerckhove, Joachim},
  doi = {10.1371/journal.pone.0149794},
  editor = {Marinazzo, Daniele},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/pone.0149794.pdf:pdf},
  issn = {1932-6203},
  journal = {PLOS ONE},
  mendeley-groups = {statistica},
  month = {feb},
  number = {2},
  pages = {e0149794},
  pmid = {26919473},
  title = {{A Bayesian Perspective on the Reproducibility Project: Psychology}},
  url = {https://dx.plos.org/10.1371/journal.pone.0149794},
  volume = {11},
  year = {2016}
}
@article{Kruschke2010,
  abstract = {Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.},
  author = {Kruschke, John K},
  doi = {10.1016/j.tics.2010.05.001},
  issn = {1879-307X},
  journal = {Trends in cognitive sciences},
  keywords = {Bayes Theorem,Humans,Reproducibility of Results,Research Design,Statistics as Topic},
  mendeley-groups = {Thesis,statistica},
  month = {jul},
  number = {7},
  pages = {293--300},
  pmid = {20542462},
  publisher = {Elsevier Ltd},
  title = {{What to believe: Bayesian methods for data analysis.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/20542462},
  volume = {14},
  year = {2010}
}
@article{Wagenmakers2018,
  abstract = {Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).},
  author = {Wagenmakers, Eric-Jan and Marsman, Maarten and Jamil, Tahira and Ly, Alexander and Verhagen, Josine and Love, Jonathon and Selker, Ravi and Gronau, Quentin F. and {\v{S}}m{\'{i}}ra, Martin and Epskamp, Sacha and Matzke, Dora and Rouder, Jeffrey N. and Morey, Richard D.},
  doi = {10.3758/s13423-017-1343-3},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/Wagenmakers2018_Article_BayesianInferenceForPsychology.pdf:pdf},
  issn = {1069-9384},
  journal = {Psychonomic Bulletin & Review},
  keywords = {Bayes factor,Hypothesis test,Posterior distribution,Statistical evidence},
  mendeley-groups = {statistica},
  month = {feb},
  number = {1},
  pages = {35--57},
  pmid = {28779455},
  title = {{Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications}},
  url = {http://link.springer.com/10.3758/s13423-017-1343-3},
  volume = {25},
  year = {2018}
}
@book{Gelman2006,
  abstract = {Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.},
  address = {Cambridge},
  author = {Gelman, Andrew and Hill, Jennifer},
  doi = {10.1017/CBO9780511790942},
  isbn = {9780511790942},
  mendeley-groups = {Thesis},
  publisher = {Cambridge University Press},
  title = {{Data Analysis Using Regression and Multilevel/Hierarchical Models}},
  url = {http://books.google.it/books/about/Data_Analysis_Using_Regression_and_Multi.html?id=c9xLKzZWoZ4C&pgis=1 http://ebooks.cambridge.org/ref/id/CBO9780511790942},
  year = {2006}
}
@book{Kruschke2014,
  abstract = {There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. Included are step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. This book is intended for first-year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Knowledge of algebra and basic calculus is a prerequisite.},
  archivePrefix = {arXiv},
  arxivId = {arXiv:1011.1669v3},
  author = {Kruschke, John K.},
  booktitle = {Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition},
  doi = {10.1016/B978-0-12-405888-0.09999-2},
  edition = {2},
  eprint = {arXiv:1011.1669v3},
  file = {::},
  isbn = {9780124058880},
  issn = {1385-4046},
  mendeley-groups = {statistica},
  pages = {1--759},
  pmid = {15003161},
  publisher = {Elsevier Inc.},
  title = {{Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition}},
  url = {http://dx.doi.org/10.1016/B978-0-12-405888-0.09999-2},
  year = {2014}
}
@article{Cumming2014,
  abstract = {We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.},
  author = {Cumming, Geoff},
  doi = {10.1177/0956797613504966},
  issn = {1467-9280},
  journal = {Psychological science},
  mendeley-groups = {Thesis},
  month = {jan},
  number = {1},
  pages = {7--29},
  pmid = {24220629},
  title = {{The new statistics: why and how.}},
  url = {http://pss.sagepub.com/content/early/2013/11/07/0956797613504966},
  volume = {25},
  year = {2014}
}
@book{gelman2013bayesian,
  author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
  mendeley-groups = {statistica},
  publisher = {CRC press},
  title = {{Bayesian data analysis}},
  year = {2013}
}
@article{Kruschke2018,
  abstract = {In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming, 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequen-tist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis. Keywords Null hypothesis significance testing {\textperiodcentered} Bayesian inference {\textperiodcentered} Bayes factor {\textperiodcentered} Confidence interval {\textperiodcentered} Credible interval {\textperiodcentered} Highest density interval {\textperiodcentered} Region of practical equivalence {\textperiodcentered} Meta-analysis {\textperiodcentered} Power analysis {\textperiodcentered} Effect size {\textperiodcentered} Randomized controlled trial {\textperiodcentered} Equivalence testing The New Statistics emphasizes a shift of emphasis away from null hypothesis significance testing (NHST) to "esti-mation based on effect sizes, confidence intervals, and meta-analysis" (Cumming, 2014, p. 7). There are many reasons to eschew NHST, with its seductive lapse to black-and-white thinking about the presence or absence of effects. There are also many reasons to promote instead a cumulative science that incrementally improves estimates of magnitudes and uncertainty. These reasons were recently highlighted in a prominent statement from the American Statistical Association (ASA; Wasserstein & Lazar, 2016) that will be summarized later in this article. Recent decades have also seen repeated calls to shift emphasis away from frequentist methods to Bayesian analysis (e.g., Lindley, 1975). In this article, we review both of these recommended shifts of emphasis in the practice of data analysis, and we promote their convergence in Bayesian methods for estimation. The goals of the New Statistics are better achieved by Bayesian methods than by frequentist methods. In that sense, we recommend a Bayesian New Statistics. Within the domain of Bayesian methods, we have a more nuanced emphasis. Bayesian methods provide a coherent framework for hypothesis testing, so when null hypothesis testing is the crux of the research then Bayesian null hypothesis testing should be carefully used. But we also believe that typical analyses should not routinely stop with hypothesis testing alone. In that sense, we recommend a New Bayesian Statistics , that is, Bayesian analyses that also consider estimates of magnitudes and uncertainty, along with meta-analyses. This article begins with an extensive description of fre-quentist and Bayesian approaches to null hypothesis testing and estimation with confidence or credible intervals. Subsequently , the article explains Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis. We hope to demonstrate that Bayesian approaches to all these analyses are more direct, more intuitive, and more informative than frequentist approaches.},
  author = {Kruschke, John K and Liddell, Torrin M},
  doi = {10.3758/s13423-016-1221-4},
  file = {::;::},
  journal = {Psychon Bull Rev},
  keywords = {Bayes factor,Bayesian inference,Confidence interval,Credible interval,Effect size,Equivalence testing,Highest density interval,Meta-analysis,Null hypothesis significance testing,Power analysis,Randomized controlled trial,Region of practical equivalence},
  mendeley-groups = {statistica},
  pages = {178--206},
  title = {{The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective}},
  volume = {25},
  year = {2018}
}
@article{Scandola2021,
  author = {Scandola, Michele and Romano, Daniele},
  doi = {10.1016/j.neuropsychologia.2021.107834},
  issn = {00283932},
  journal = {Neuropsychologia},
  month = {mar},
  pages = {107834},
  title = {{Bayesian Multilevel Single Case Models using 'Stan'. A new tool to study single cases in Neuropsychology}},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0028393221000853},
  year = {2021}
}
@incollection{Raftery1995,
  address = {Cambridge},
  author = {Raftery, Adrian E},
  booktitle = {Sociological Methodology},
  editor = {Marsden, P V},
  file = {:home/michele/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Raftery - 1995 - Bayesian Model Selection in Social Research.pdf:pdf},
  number = {1995},
  pages = {111--163},
  publisher = {Blackwells},
  title = {{Bayesian Model Selection in Social Research}},
  volume = {25},
  year = {1995}
}
@article{Stefan,
  author = {Stefan, Angelika M and Katsimpokis, Dimitris and Gronau, Quentin F and Wagenmakers, Eric-Jan},
  doi = {10.31234/osf.io/8xkqd},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/Expert_Agreement_in_Prior_Elicitation(1).pdf:pdf},
  journal = {PsyArXiv},
  mendeley-groups = {statistica},
  title = {{Expert Agreement in Prior Elicitation and its Effects on Bayesian Inference}},
  url = {https://osf.io/vqszj/.},
  year = {2021}
}
@article{Dickey1971,
  abstract = {A general weak convergence theory is developed for time-sequential censored rank statistics in the two-sample problem of comparing time to failure between two treatment groups, such as in the case of a clinical trial in which patients enter serially and, after being randomly allocated to one of two treatments, are followed until they fail or withdraw from the study or until the study is terminated. Applications of the theory to time-sequential tests based on these censored rank statistics are also discussed.},
  author = {Dickey, James M.},
  doi = {10.1214/aoms/1177693507},
  file = {:media/michele/DATA/Dropbox/SCNlab/LETTERATURA/statistics/Dickey 1971.pdf:pdf},
  issn = {0003-4851},
  journal = {The Annals of Mathematical Statistics},
  mendeley-groups = {statistica},
  month = {feb},
  number = {1},
  pages = {204--223},
  title = {{The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters}},
  url = {http://projecteuclid.org/euclid.aoms/1177693507},
  volume = {42},
  year = {1971}
}
@article{Carlin1995,
  author = {Carlin, Bradley P. and Chib, Siddhartha},
  file = {::},
  journal = {Journal of the RoyalStatistical Society B},
  mendeley-groups = {statistica},
  number = {3},
  pages = {473--484},
  title = {{Bayesian model choice via Markov Chain Monte Carlo method}},
  volume = {57},
  year = {1995}
}
@article{OHara2009,
  abstract = {The selection of variables in regression problems has occupied the minds of many statisticians. Several Bayesian variable selection methods have been developed, and we concentrate on the following methods: Kuo & Mallick, Gibbs Variable Selection (GVS), Stochastic Search Variable Selection (SSVS), adaptive shrinkage with Je{\AE}reys' prior or a Laplacian prior, and reversible jump MCMC. We review these methods, in the context of their di{\AE}erent properties. We then implement the methods in BUGS, using both real and simulated data as examples, and investigate how the di{\AE}erent methods perform in practice. Our results suggest that SSVS, reversible jump MCMC and adaptive shrinkage methods can all work well, but the choice of which method is better will depend on the priors that are used, and also on how they are implemented.},
  author = {O'Hara, R. B. and Sillanp{\"{a}}{\"{a}}, M. J.},
  doi = {10.1214/09-BA403},
  isbn = {1936-0975},
  issn = {19360975},
  journal = {Bayesian Analysis},
  keywords = {BUGS,MCMC,Variable selection},
  mendeley-groups = {statistica},
  number = {1},
  pages = {85--118},
  pmid = {273483200007},
  title = {{A review of bayesian variable selection methods: What, how and which}},
  volume = {4},
  year = {2009}
}
@article{Kuo198,
  abstract = {simple method for subset selection of independent variables in regression models is proposed. We expand the usual regression equation to an equation that incorporates all possible subsets of predictors by adding indicator variables as parameters. The vector of indicator variables dictates which predictors to include. Several choices of priors can be employed for the unknown regression coefficients and the unknown indicator parameters. The posterior distribution of the indicator vector is approximated by means of the Markov Chain Monte Carlo algorithm. We select subsets with high posterior probabilities. In addition to linear models, we consider generalized linear models.},
  author = {Kuo, Lynn and Mallick, Bani},
  journal = {Sankhyā: The Indian Journal of Statistics, Series B},
  mendeley-groups = {statistica},
  number = {1},
  pages = {65--81},
  title = {{Variable Selection for Regression Models}},
  volume = {60},
  year = {1998}
}
@article{Vehtari2016,
  abstract = {Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparing of predictive errors between two models. We implement the computations in an R package called 'loo' and demonstrate using models fit with the Bayesian inference package Stan.},
  archivePrefix = {arXiv},
  arxivId = {1507.04544},
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  doi = {10.1007/s11222-016-9696-4},
  eprint = {1507.04544},
  isbn = {1507.04544},
  issn = {0960-3174},
  journal = {Statistics and Computing},
  keywords = {Bayesian computation,K-fold cross-validation,Leave-one-out cross-validation (LOO),Pareto smoothed importance sampling (PSIS),Stan,Widely applicable information criterion (WAIC)},
  mendeley-groups = {statistica},
  month = {sep},
  number = {5},
  pages = {1413--1432},
  publisher = {Springer US},
  title = {{Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC}},
  url = {http://link.springer.com/10.1007/s11222-016-9696-4},
  volume = {27},
  year = {2017}
}
@article{Gelman1992,
  abstract = {The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were contin- ued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random- effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.},
  author = {Gelman, Andrew and Rubin, Donald B},
  file = {::},
  journal = {Statistical Science},
  keywords = {algorithm,and phrases,bayesian inference,convergence of stochastic,ecm,em,gibbs sampler,importance sampling,metropolis,multiple imputation,processes,random-effects model,sir},
  mendeley-groups = {statistica},
  number = {4},
  pages = {457--472},
  title = {{Inference from Iterative Simulation Using Multiple Sequences}},
  volume = {7},
  year = {1992}
}
@article{Gelman2013,
  abstract = {Abstract: Posterior predictive p-values do not in general have uniform distributions under the null hypothesis (except in the special case of ancillary test variables) but instead tend to have distributions more concentrated near 0.5. From different perspectives, such ...\n},
  archivePrefix = {arXiv},
  arxivId = {math.PR/0000000},
  author = {Gelman, Andrew},
  doi = {10.1214/13-EJS854},
  eprint = {0000000},
  issn = {1935-7524},
  journal = {Electronic Journal of Statistics},
  keywords = {and phrases,bayesian inference,model checking,p-value,posterior,predictive check,u-value},
  mendeley-groups = {statistica},
  pages = {1--8},
  primaryClass = {math.PR},
  title = {{Understanding posterior p-values}},
  year = {2013}
}
@incollection{gilks2005,
  author = {Gilks, W. R.},
  booktitle = {Encyclopedia of Biostatistics},
  doi = {10.1002/0470011815.b2a14021},
  isbn = {9780470011812},
  mendeley-groups = {Thesis},
  publisher = {John Wiley & Sons, Ltd},
  title = {{Markov Chain Monte Carlo}},
  year = {2005}
}
@book{kaplan2014bayesian,
  title={Bayesian statistics for the social sciences},
  author={Kaplan, David},
  year={2014},
  publisher={Guilford Publications}
}
@article{Metropolis1987,
author = {Metropolis, N},
journal = {Los Alamos Science},
mendeley-groups = {Thesis},
pages = {125--130},
title = {{The beginning of the Monte Carlo method}},
volume = {special issue},
year = {1987}
}
@book{Gamerman2006,
  address = {Boca Raton (FL)},
  author = {Gamerman, Dani and Lopes, Hedibert F.},
  edition = {second},
  isbn = {9781584885870},
  mendeley-groups = {statistica},
  pages = {342},
  publisher = {Chapman and Hall/CRC},
  title = {{Markov Chain Monte Carlo Stochastic Simulation for Bayesian Inference}},
  year = {2006}
}
@misc{vehtari2021pareto,
      title={Pareto Smoothed Importance Sampling},
      author={Aki Vehtari and Daniel Simpson and Andrew Gelman and Yuling Yao and Jonah Gabry},
      year={2021},
      eprint={1507.02646},
      archivePrefix={arXiv},
      primaryClass={stat.CO}
}
@book{statrethinkingbook,
  author = {McElreath, Richard},
  mendeley-groups = {statistica},
  publisher = {CRC Press},
  title = {{Statistical Rethinking: A Bayesian Course with Examples in R and Stan}},
  url = {http://xcelab.net/rm/statistical-rethinking/},
  year = {2016}
}

repository: michelescandola/BayesHSC-MCMC

repo_name: michelescandola/BayesHSC-MCMC


text:
DESCRIPTION = "Tegra GBM backend for mesa"
HOMEPAGE = "https://github.com/oe4t/tegra-udrm-gbm"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=c43c2c3b83cc7c8460566fb6da10f02a"

RPROVIDES:${PN} += "tegra-gbm-backend"

DEPENDS = "mesa libdrm"
RDEPENDS:${PN} = "tegra-libraries-gbm"

COMPATIBLE_MACHINE = "(tegra)"

SRC_REPO = "github.com/oe4t/tegra-udrm-gbm.git;protocol=https"
SRCBRANCH = "master"
SRC_URI = "git://${SRC_REPO};branch=${SRCBRANCH}"
# v1.0.2 tag
SRCREV = "103d33d07e6c38106bf952403b336dc3ca809a08"

S = "${WORKDIR}/git"

inherit meson pkgconfig

do_install:append() {
    ln -s tegra-udrm_gbm.so ${D}${libdir}/gbm/nvidia-drm_gbm.so
}

FILES:${PN} += "${libdir}/gbm"
INSANE_SKIP:${PN} += "dev-so"

repository: SOL-Space-Operating-Linux/meta-tegra

repo_name: SOL-Space-Operating-Linux/meta-tegra

gha_license_id: MIT

gha_language: BitBake

extension: bb


text:
@MISC{Minuit:2004,
	author = {James, Fred},
	title = {{MINUIT Tutorial: Function Minimization}},
	year = {2004},
	howpublished = {\url{http://seal.cern.ch/documents/minuit/mntutorial.pdf}}
}

@MISC{Minuit2:2004,
        author = {CERN},
        title = {{MINUIT2}},
        year = {2004},
        howpublished = {\url{http://seal.web.cern.ch/seal/snapshot/work-packages/mathlibs/minuit/}}
}

@book{QTProgramming,
author={Blanchette, Jasmin and Summerfield, Mark},
year = {2006},
title = {{A Brief History of Qt. C++ GUI Programming with Qt 4}},
publisher = {Prentice-Hall}
}

@MISC{BlackArtCurveFitting:2012,
        author = {WordPress},
        title = {{Art of curve-fitting… or black magic of curve-fitting XPS spectra}},
        year = {2012},
        howpublished = {\url{https://kartyush.wordpress.com/2012/05/22/art-of-curve-fitting-or-black-magic-of-curve-fitting-xps-spectra/}}
}

@MISC{HistoryOfXPS:2012,
        author = {WordPress},
        title = {{History of Electron Spectroscopy}},
        year = {2012},
        howpublished = {\url{https://kartyush.wordpress.com/2012/05/18/history-of-photoelectron-spectroscopy/}}
}

@MISC{ESSistMe-LOGO,
        author = {lightsources.org},
        title = {{A New Way to Identify the Atomic Origin of Molecular Valence Electrons}},
        year = {2014},
howpublished = {\url{http://www.lightsources.org/press-release/2014/05/09/new-way-identify-atomic-origin-molecular-valence-electrons}}
}

@MISC{QCustomPlot:2016,
        author = {{Emanuel Eichhammer}},
	title = {{A Qt C++ widget for plotting and data visualization}},
	year = {2016},
	howpublished = {\url{http://www.qcustomplot.com/}}
 }

@MISC{QtDoc:2016,
        author = {{The Qt Company Ltd.}},
        title = {{Qt 5 documentation}},
        year = {2016},
        howpublished = {\url{http://doc.qt.io/}}
 }

@MISC{CMake:2016,
        author = {{Kitware}},
        title = {{An open-source, cross-platform family of tools designed to build, test and package software}},
        year = {2016},
        howpublished = {\url{https://cmake.org/}}
 }

@MISC{FileSystemHierarchy:2016,
        author = {{Wikipedia}},
        title = {{Filesystem Hierarchy Standard}},
        year = {2016},
        howpublished = {\url{https://en.wikipedia.org/wiki/Filesystem_Hierarchy_Standard}}
 }

@MISC{gtest:2016,
author = {Google},
title = {{Google's C++ test framework}},
year = {2016},
howpublished = {\url{https://github.com/google/googletest}}
}

repository: lulzzz/ESsistMe

repo_name: lulzzz/ESsistMe


text:
@InProceedings{duvenaud10a,
  section = {challenge},
  title = {Causal learning without DAGs},
  author = {David Duvenaud and Daniel Eaton and Kevin Murphy and Mark Schmidt},
  pages = {177--190},
  abstract = {Causal learning methods are often evaluated in terms of their ability to discover a true underlying directed acyclic graph (DAG) structure. However, in general the true structure is unknown and may not be a DAG structure.  We therefore consider evaluating causal learning methods in terms of predicting the effects of interventions on unseen test data.  Given this task, we show that there exist a variety of approaches to modeling causality, generalizing DAG-based methods.  Our experiments on synthetic and biological data indicate that some non-DAG models perform as well or better than DAG-based methods at causal prediction tasks.},
  pdf = {http://jmlr.org/proceedings/papers/v6/duvenaud10a/duvenaud10a.pdf},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@article{li2017false,
 author = {Li, Andrea S and Johnson, Alistair E and Mark, Roger G},
 author+an = {2=highlight},
 journal = {arXiv preprint arXiv:1709.03562},
 title = {False arrhythmia alarm reduction in the intensive care unit},
 year = {2017}
}


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repository: ferlimputra/markdown-previewer

repo_name: ferlimputra/markdown-previewer

extension: css


text:
@article{silva_active_2023,
	title = {Active {Textile} {Fabrics} from {Weaving} {Liquid} {Crystalline} {Elastomer} {Filaments}},
	issn = {0935-9648, 1521-4095},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/adma.202210689},
	doi = {10.1002/adma.202210689},
	language = {en},
	urldate = {2023-01-23},
	journal = {Advanced Materials},
	author = {Silva, Pedro E. S. and Lin, Xueyan and Vaara, Maija and Mohan, Mithila and Vapaavuori, Jaana and Terentjev, Eugene M.},
	month = jan,
	year = {2023},
	pages = {2210689},
}


text:
%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/

%% Created for 張子軒 at 2020-12-11 10:00:24 +0800

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@electronic{Qiskit-Textbook,
	author = {Abraham Asfaw and Luciano Bello and Yael Ben-Haim and Sergey Bravyi and Nicholas Bronn and Lauren Capelluto and Almudena Carrera Vazquez and Jack Ceroni and Richard Chen and Albert Frisch and Jay Gambetta and Shelly Garion and Leron Gil and Salvador De La Puente Gonzalez and Francis Harkins and Takashi Imamichi and David McKay and Antonio Mezzacapo and Zlatko Minev and Ramis Movassagh and Giacomo Nannicni and Paul Nation and Anna Phan and Marco Pistoia and Arthur Rattew and Joachim Schaefer and Javad Shabani and John Smolin and Kristan Temme and Madeleine Tod and Stephen Wood and James Wootton.},
	date-added = {2020-12-11 09:56:11 +0800},
	date-modified = {2020-12-11 09:56:11 +0800},
	title = {Learn Quantum Computation Using Qiskit},
	url = {http://community.qiskit.org/textbook},
	year = {2020},
	Bdsk-Url-1 = {http://community.qiskit.org/textbook}}

@article{Biamonte:2017aa,
	abstract = {Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.},
	author = {Biamonte, Jacob and Wittek, Peter and Pancotti, Nicola and Rebentrost, Patrick and Wiebe, Nathan and Lloyd, Seth},
	da = {2017/09/01},
	date-added = {2020-12-11 09:52:30 +0800},
	date-modified = {2020-12-11 09:52:30 +0800},
	doi = {10.1038/nature23474},
	id = {Biamonte2017},
	isbn = {1476-4687},
	journal = {Nature},
	number = {7671},
	pages = {195--202},
	title = {Quantum machine learning},
	ty = {JOUR},
	url = {https://doi.org/10.1038/nature23474},
	volume = {549},
	year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1038/nature23474}}

@article{coles2018quantum,
	author = {Coles, Patrick J and Eidenbenz, Stephan and Pakin, Scott and Adedoyin, Adetokunbo and Ambrosiano, John and Anisimov, Petr and Casper, William and Chennupati, Gopinath and Coffrin, Carleton and Djidjev, Hristo and others},
	date-added = {2020-12-11 09:52:28 +0800},
	date-modified = {2020-12-11 09:52:28 +0800},
	journal = {arXiv},
	pages = {arXiv--1804},
	title = {Quantum algorithm implementations for beginners},
	year = {2018}}

@misc{nielsen2002quantum,
	author = {Nielsen, Michael A and Chuang, Isaac},
	date-added = {2020-12-11 09:52:26 +0800},
	date-modified = {2020-12-11 09:52:26 +0800},
	publisher = {American Association of Physics Teachers},
	title = {Quantum computation and quantum information},
	year = {2002}}

repository: THCtw/QML

repo_name: THCtw/QML

gha_language: Jupyter Notebook


text:
@article{Owen-Smith2014-bf,
 abstract = {Daily activity schedules and time budgets reveal how animals
cope with changing environmental conditions in securing food and
evading enemies. Theory suggests that animals in populations
limited by food availability should be energy maximizers in
their foraging time allocation, while those regulated by
predation should minimize their mobile activity levels. We
compared daily and seasonal variation in activity states among
three species of grazing ungulates coexisting in the same region
of K ruger N ational P ark, S outh …},
 author = {Owen-Smith, N and Goodall, V},
 journal = {J. Zool.},
 number = {3},
 pages = {181--191},
 publisher = {Wiley Online Library},
 title = {Coping with savanna seasonality: comparative daily activity
patterns of a frican ungulates as revealed by gps telemetry},
 volume = {293},
 year = {2014}
}

repository: SAEON/academic-kickstart

repo_name: SAEON/academic-kickstart


text:
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Preferences -> BibTeX in Mendeley Desktop

@article{Wang2009,
abstract = {The cost and effectiveness of two bed bug (Cimex lectularius L.) integrated pest management (IPM) programs were evaluated for 10 wk. Sixteen bed bug-infested apartments were chosen from a high-rise low-income apartment building. The apartments were randomly divided into two treatment groups: diatomaceous earth dust-based IPM (D-IPM) and chlorfenapyr spray-based IPM (S-IPM). The initial median (minimum, maximum) bed bug counts (by visual inspection) of the two treatment groups were 73.5 (10, 352) and 77 (18, 3025), respectively. A seminar and an educational brochure were delivered to residents and staff. It was followed by installing encasements on mattresses and box springs and applying hot steam to bed bug-infested areas in all 16 apartments. Diatomaceous earth dust (Mother Earth-D) was applied in the D-IPM group 2 d after steaming. In addition, bed bug-intercepting devices were installed under legs of infested beds or sofas or chairs to intercept bed bugs. The S-IPM group only received 0.5{\%} chlorfenapyr spray (Phantom) after the nonchemical treatments. All apartments were monitored bi-weekly and retreated when necessary. After 10 wk, bed bugs were eradicated from 50{\%} of the apartments in each group. Bed bug count reduction (mean +/- SEM) was 97.6 +/- 1.6 and 89.7 +/- 7.3{\%} in the D-IPM and S-IPM groups, respectively. Mean treatment costs in the 10-wk period were {\$}463 and {\$}482 per apartment in the D-IPM and S-IPM groups, respectively. Bed bug interceptors trapped an average of 219 +/- 135 bed bugs per apartment in 10 wk. The interceptors contributed to the IPM program efficacy and were much more effective than visual inspections in estimating bed bug numbers and determining the existence of bed bug infestations.},
author = {Wang, Changlu and Gibb, Timothy and Bennett, Gary W},
doi = {10.1603/033.046.0322},
issn = {0022-2585},
journal = {Journal of medical entomology},
keywords = {and,apartments,as well as in,australia,bed bug,canada,cimex lectularius,cimex lectularius l,control,creased rapidly in recent,europe,infestations have in-,integrated pest management,states,years throughout the united},
number = {3},
pages = {566--571},
pmid = {19496428},
title = {{Evaluation of two least toxic integrated pest management programs for managing bed bugs (Heteroptera: Cimicidae) with discussion of a bed bug intercepting device.}},
volume = {46},
year = {2009}
}

license: Unlicense

repository: sepmein/references

repo_name: sepmein/references


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text:
@article{MASSCHELEIN2020101127,
title = {Testing for complementarities between accounting practices},
journal = {Accounting, Organizations and Society},
volume = {86},
pages = {101127},
year = {2020},
issn = {0361-3682},
doi = {https://doi.org/10.1016/j.aos.2020.101127},
url = {https://www.sciencedirect.com/science/article/pii/S036136821830165X},
author = {Stijn Masschelein and Frank Moers},
keywords = {Complementarity theory, Management control, Statistical methods},
abstract = {Following the theoretical innovations of complementarity theory, management control studies have investigated interdependencies between different management control practices. In this paper, we compare the two dominant statistical specifications to test for the presence of an interdependency. We show theoretically how the power of the demand and the performance specification varies with the level of optimality in the sample and how those specifications are vulnerable to correlated omitted variable bias. Our simulation results reveal that the demand specification is more robust to variations in optimality and correlated omitted variables than the performance specification. We use these results to formulate recommendations for future research into management control interdependencies.}
}

repository: stijnmasschelein/site

repo_name: stijnmasschelein/site

gha_language: HTML


text:
@article{Ronnow.2014.Defining,
  title = {Defining and Detecting Quantum Speedup},
  author = {R{\o}nnow, Troels F. and Wang, Zhihui and Job, Joshua and Boixo, Sergio and Isakov, Sergei V. and Wecker, David and Martinis, John M. and Lidar, Daniel A. and Troyer, Matthias},
  year = {2014},
  month = jul,
  volume = {345},
  pages = {420--424},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1252319},
  abstract = {The development of small-scale digital and analog quantum devices raises the question of how to fairly assess and compare the computational power of classical and quantum devices, and of how to detect quantum speedup. Here we show how to define and measure quantum speedup in various scenarios, and how to avoid pitfalls that might mask or fake quantum speedup. We illustrate our discussion with data from a randomized benchmark test on a D-Wave Two device with up to 503 qubits. Comparing the performance of the device on random spin glass instances with limited precision to simulated classical and quantum annealers, we find no evidence of quantum speedup when the entire data set is considered, and obtain inconclusive results when comparing subsets of instances on an instance-by-instance basis. Our results for one particular benchmark do not rule out the possibility of speedup for other classes of problems and illustrate that quantum speedup is elusive and can depend on the question posed.},
  archiveprefix = {arXiv},
  eprint = {1401.2910},
  eprinttype = {arxiv},
  journal = {Science},
  keywords = {Quantum Physics},
  note = {\url{http://arxiv.org/abs/1401.2910}},
  number = {6195},
  primaryclass = {quant-ph}
}

@article{Sen.2016.BV,
  title = {{{BV Master Action}} for {{Heterotic}} and {{Type II String Field Theories}}},
  author = {Sen, Ashoke},
  year = {2016},
  month = feb,
  volume = {2016},
  issn = {1029-8479},
  doi = {10.1007/JHEP02(2016)087},
  abstract = {We construct the quantum BV master action for heterotic and type II string field theories.},
  archiveprefix = {arXiv},
  eprint = {1508.05387},
  eprinttype = {arxiv},
  journal = {Journal of High Energy Physics},
  keywords = {_tablet,Batalin-Vilkoviski formalism,covariant string field theory,futur: 3,note: 4,printed,superstring field theory},
  note = {\url{http://arxiv.org/abs/1508.05387}},
  number = {2}
}

@article{Smolin.2013.Classical,
  title = {Classical Signature of Quantum Annealing},
  author = {Smolin, John A. and Smith, Graeme},
  year = {2013},
  month = may,
  abstract = {A pair of recent articles concluded that the D-Wave One machine actually operates in the quantum regime, rather than performing some classical evolution. Here we give a classical model that leads to the same behaviors used in those works to infer quantum effects. Thus, the evidence presented does not demonstrate the presence of quantum effects.},
  archiveprefix = {arXiv},
  eprint = {1305.4904},
  eprinttype = {arxiv},
  journal = {arXiv:1305.4904 [quant-ph]},
  keywords = {Quantum Physics},
  note = {\url{http://arxiv.org/abs/1305.4904}},
  primaryclass = {quant-ph}
}

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repository: MicaW/tic-tac-toe

repo_name: MicaW/tic-tac-toe