Probability Basics

Joint distribution

The joint distribution of two random variables $X$ and $Y$ :

p (x, y) = p (X = x, Y = y)

Marginal distribution

The Marginal distribution of $X$ is summing over all possible states of $Y$ :

p (X = x) = \sum_{y} p (X = x, Y = y)

This is also called Sum rule / The rule of total probability.

Conditional joint

p (X, Y | Z) = p (X | Z) p (Y | Z)

Product rule

p (A, B) = p (A | B) p (B) = p (B | A) p (A)

Chain rule

p (A_{1}, A_{2}, A_{3}, . . ., A_{n}) = p (A_{1}) \times p (A_{2} | A_{1}) \times p (A_{3} | A_{1}, A_{2}) \times . . . \times p (A_{n} | A_{1}, A_{2}, . . ., A_{n - 1})

Note that Markov Chain can be regarded as a simplified version of the chain rule, i.e. predict by ignoring all previous events but the last one(s), so that $p (A_{n} | A_{1}, . . A_{n - 1}) \approx p (A_{n} | A_{n - 1})$

application for multiple variables:

p (A | B, C) p (B | C) = p (B | A, C) p (A | C)

derivation:

p (A | B, C) = p (A, B, C) / p (B, C)

with

p (A, B, C) = p (B | A, C) p (A | C) p (C), p (B, C) = p (B | C) p (C)