Any subset $E$ of the sample space is known as **an event.**

That is, an event is a set consisting of possible outcomes of the experiment. If the outcome of the experiment is contained in $E$, then we say that $E$* *has occurred.

For each event $E$, we denote $\operatorname{P}(E)$ as the probability of event $E$ occurring.

The probability that at least one of the elementary events in the sample space will occur is $1$.

Every probability value is between $0$ and $1$ included.

For any sequence of mutually exclusive events$E_i$we have:

$\operatorname{P} \left( \bigcup_{i=1}^n E_i \right) = \sum_{i = 1}^n \operatorname{P} (E_i)$

**A permutation** is an arrangement of$k$objects from a pool of$n$objects, in a given order. The number of such arrangements is:

$P(n, k) = \frac{n!}{(n - k)!}$

**A combination** is an arrangement of$k$objects from a pool of$n$objects, where the order does not matter. The number of such arrangements is:

$C(n, k) = \frac{n!}{k! \cdot (n - k)!}$

We note that for $0 \le k \le n$ *, *we have$P(n, r) \ge C(n, r)$

**Conditional probability** is the **probability** of one event occurring with some relationship to one or more other events.

**Independence.** Two events$A$and$B$are independent if and only if we have:

$\operatorname{P} (A \cap B) = \operatorname{P}(A) \cdot \operatorname{P}(B)$

**Law of total probability. **Given an event$A$, with known conditional probabilities given any of the $B_i$ events, each with a known probability itself, what is the total probability that$A$will happen? The answer is

$\operatorname{P}(A) = \sum_{i = 1}^n \operatorname{P}(A | B_i) \cdot \operatorname{P}(B_i)$

**Bayes' rule.** For events$A$and$B$such that$\operatorname{P}(B) > 0$, we have

$\operatorname{P} (A | B) = \frac{\operatorname{P}(B | A) \cdot \operatorname{P}(A)}{\operatorname{P}(B)}$

The formulas will be explicitly detailed for the discrete **(D)** and continuous **(C)** cases.

**Expected value.** The expected value of a random variable, also known as the mean value or the first moment, is often noted $\operatorname{E}[X]$and is the value that we would obtain by averaging the results of the experiment infinitely many times.

$\textbf{(D) } \operatorname{E} [X] = \sum_{i = 1}^n x_i \cdot f(x_i) \hspace{5em} \textbf{(C) } \operatorname{E} [X]= \int_{-\infty}^{+\infty} x \cdot f(x) \ dx$

**Variance.** The variance of a random variable, often noted $\operatorname{Var}[X]$, is a measure of the spread of its distribution function.

$\operatorname{Var}(X) = \operatorname{E}[(X −\operatorname{E}[X])^2] = \operatorname{E}[X^2] − \operatorname{E}[X]^2$

**Markov's inequality. **Let$X$be a random variable and$a > 0$

$\operatorname{P} (X\geq a)\leq {\frac {\operatorname {E} (X)}{a}}$

**Chebyshev's inequality**. Let$X$be a random variable with expected value$\mu$,

$\operatorname{P} (| X - \mu| \ge k \sigma) \leq \frac{1}{k^2}$