TITLE: Reference request for complete probability text without measure theory
QUESTION [5 upvotes]: I'm looking for a complete probability reference text, covering the majority of standard probability and stochastic process topics that can be covered without the use of measure theory. 
I've already had a basic course in probability and in stochastic processes, so I'm looking for more of a desk reference type of book. 
The three that have been recommended to me are 

Probability, Statistics, and Random Processes by Papoulis
Probability for Statistics and Machine Learning by DasGupta
Probability By Feller

I'm not too interested in Feller, since it seems that vol 2 has a fair bit of measure theory and vol 1 is only discrete. Any other recommendations?

REPLY [2 votes]: I will answer my own question for public documentation, but leave the discussion open for further recommendations.
I decided to use Probability for Statistics and Machine Learning By Anirban DasGupta as my non-measure theoretic reference text. The deciding factors were:

The advantage of having to carry around only one physical copy, whereas with Ross/Feller there are two volumes
Treats the standard first semester univariate probability material as a quick but self-contained review occupying only ~13% of the text. Great for those of us who are not learning the material for the first time. 
Large breadth of topics: Multivariate probability, stochastic processes, asymptotics, large deviations, simulation, statistics, and machine learning 
Extensive list of reference material
Author is consistent about pointing out when certain definitions and propositions are poorly expressed due to the absence of measure theory.

Some downsides to the text:

Poor index and glossary. I find myself again and again handwriting additional notes into the index and glossary. For example, the author spends a few pages discussing the Berry-Esseen theorem in great detail and yet omits it from the index.
Some topics are covered too briefly and with little depth to be of much use as a reference (better served as short introductions to the topic), most notably the section on machine learning.
It is in its first edition, and I have been unable to locate a list of errata, if it exists.