TITLE: Concept of Probability distribution
QUESTION [5 upvotes]: Sorry for a silly question, but it seems like only you can answer this question.
What's is the concept of Probability distribution, what's the meanining behind this term. Why we need probability function if we already have pdf (probability density function) and pmf (probability mass function). What's the new new information PD provide us with. Why we use it's definition $P(X<a)$ with less sign?
Sorry for the lack of the information.
Thanks!

REPLY [4 votes]: For a real-valued random variable $X$, let $P_X$ denote the probability distribution of $X$, meaning that $P_X (B) = P(X \in B)$, for any Borel set $B$ of $\mathbb{R}$. Particularly important is the case when $B$ is of the form $B = (-\infty, x]$, $x \in \mathbb{R}$; then $P_X (B) = P(X \in (-\infty,x]) = P(X \leq x)$. The function $F_X$ defined by $F_X (x) = P(X \leq x)$, $x \in \mathbb{R}$, is called the distribution function of $X$. Regarding your last question, it is important to note that the distribution function of $X$ is sometimes defined as the probability of $X < x$, but this is less common.
Elementary properties of a distribution function: 1) $F_X$ is monotone increasing, that is $F_X (x) \leq F_X (y)$ when $x < y$; 2) $F_X$ is right-continuous at every $x \in \mathbb{R}$, that is $F_X (y)$ approaches $F_X (x)$ as $y$ approaches $x$ from the right; 3) $F_X (x) \to 0$ as $x \to -\infty$, and $F_X (x) \to 1$ as $x \to \infty$. Further note that, for any $a,b \in \mathbb{R}$ with $a < b$, $P( a < X \leq b) = P_X ((a,b]) = F_X (b) - F_X (a)$.
It is clear from the definitions that any random variable has a probability distribution and a distribution function. A random variable $X$ has a probability density function (pdf) if and only if $X$ is absolutely continuous; $X$ has a probability mass function (pmf) if and only if $X$ is discrete. However, there are random variables which are neither absolutely continuous nor discrete. As a simple example, let $X$ be defined as follows: $X=U$ with probability $1/2$, where $U$ is a uniform$(0,1)$ random variable, and $X=1$ with probability $1/2$. Moreover, there are even continuous random variables which are not absolutely continuous (continuous singular random variables). For $X$ which is neither absolutely continuous nor discrete, the probability distribution and distribution function of $X$ are clearly essential. For example, the expectation of such $X$ can be written in terms of $P_X$ by ${\rm E}(X) = \int_\mathbb{R} {xP_X (dx)}$ and in terms of $F$ by ${\rm E}(X) = \int_{ - \infty }^\infty  {x\,dF(x)} $ (provided that the integrals converge; these formulas hold for any random variable $X$). The formula ${\rm E}(X) = \int_{ - \infty }^\infty  {x f_X (x)}\,dx$, where $f_X$ is the pdf of $X$, can be applied only to absolutely continuous random variables, and the formula ${\rm E}(X) = \sum\nolimits_k {x_k p_k } $, where $p_k = P(X=x_k)$, can be applied only to discrete random variables.
Remark: When $X$ is absolutely continuous, $F_X (x) = \int_{ - \infty }^x {f(t)\,dt}$; when $X$ is discrete, $F_X (x) = \sum\nolimits_{x{}_k \le x} {p_k } $.

REPLY [2 votes]: There are probability distributions that have neither a p.d.f. nor a p.m.f. The easiest example of this would be a random variable that has both discrete and continuous behaviour simultaneously, such as a random variable with the rectified Gaussian distribution.  However, every probability distribution on $\mathbb{R}$ does have a c.d.f., and every c.d.f. uniquely determines a probability distribution.
Let's make this precise. First, we should establish some properties common to all cumulative distribution functions. Let $X$ be a real-valued random variable. Its c.d.f. is the function $F : \mathbb{R} \to [0, 1]$ given by
$$F(x) = \mathbb{P}[X \le x]$$
By the axioms for a probability measure, it is immediate that $F$ is an non-decreasing function with
$$\begin{align} \lim_{x \searrow -\infty} F(x) & = 0 & \lim_{x \nearrow +\infty} F(x) & = 1 \end{align}$$
It is also straightforward to show that $\mathbb{P}[a < X \le b] = F(b) - F(a)$. It is clear that $\lim_{b \searrow a} \mathbb{P}[a < X \le b] = 0$ while $\lim_{a \nearrow b} \mathbb{P}[a < X \le b] = \mathbb{P}[X = b]$, so we conclude that
$$\begin{align} \lim_{x \searrow a} F(x) & = F(a) & \lim_{x \nearrow b} F(x) & \text{exists} \end{align}$$
In other words, $F$ is right-continuous with all left-limits. Such a function is called càdlàg (which, despite appearances, is French, and short for continue à droite, limite à gauche).
Proposition. Every non-decreasing càdlàg function $F : \mathbb{R} \to [0, 1]$ with $F(x) \searrow 0$ as $x \searrow -\infty$ and $F(x) \nearrow 1$ as $x \nearrow +\infty$ is the c.d.f. of some real-valued random variable.
Proof. For convenience, let us extend $F$ to be a function $\mathbb{R} \cup \{ -\infty \} \to [0, 1]$, with $F(-\infty) = 0$. Then, by the adjoint functor theorem, there is a function $G : [0, 1] \to \mathbb{R} \cup \{ -\infty \}$ which is left adjoint to $F$, that is, for all $z \in [0, 1]$ and $x \in \mathbb{R} \cup \{ -\infty \}$, 
$$G(z) \le x \text{ if and only if } z \le F(x)$$
and $G$ is a non-decreasing left-continuous function. [Exercise: Prove this directly without using a sledgehammer.]
Now let $Z$ be a random variable uniformly distributed on $[0, 1]$, and set $X = G(Z)$. This well-defined since $G$ is measurable. We then have
$$\begin{align*} \mathbb{P}[X \le x] & = \mathbb{P}[G(Z) \le x] \\
& = \mathbb{P}[Z \le F(x)] = F(x) \end{align*}$$
In other words, the c.d.f. of $X$ is precisely $F$, as required. (Note that $\mathbb{P}[X = -\infty] = 0$, so $X$ really is a real-valued random variable.)