Probability Cheatsheet

Why do you see it here? Because I need it.

Probability of an event

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 , then we say that $E$ has occurred.

---

Axioms of probability

For each event $E$, we denote $\mathcal{P}(E)$ 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:

$$\mathcal{P}\left(\bigcup _{i=1}^n{E}_i\right)=\sum _{i=1}^n\mathcal{P}(E_i)$$

---

Combinatorics

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 probabilities

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:

$$P(A\cap B)=P(A)\cdot P(B)$$

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

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

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

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

---

Expectation and Moments of the Distribution

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 $\mathcal{E}[X]$ and is the value that we would obtain by averaging the results of the experiment infinitely many times.

$$E[X]=\sum _{i=1}^n{x}_i\cdot f(x_i)$$ $$E[X]={\int }_{-\infty }^{+\infty }x\cdot f(x)dx$$

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

$$\mathcal{V}ar(X)=E[(X-E[X]{)}^2]=E[X^2]-E[X{]}^2$$

---

Some Inequalities

Markov’s inequality. Let $X$ be a random variable and $a>0$, then

$$P(X\ge a)\le \frac{E(X)}{a}$$

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

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