Probability Theory

The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as sample size increases.

A Cumulative Distribution Function (CDF) describes the probability that a random variable takes on a value less than or equal to a specified value.

A degenerate distribution is a probability distribution concentrated at a single point.

The Dirichlet distribution is a family of continuous probability distributions used for modeling proportions.

Frequentist Probability is a framework for understanding probability as the long-run frequency of events based on repeated trials.

An indicator function is a mathematical tool that shows whether a condition is true or false for a given input.

A method to generate random samples from any probability distribution using its cumulative distribution function (CDF).

Joint distribution describes the probability distribution of two or more random variables simultaneously.

A joint probability distribution describes the likelihood of two or more random variables occurring simultaneously.

The Markov Property states that future states depend only on the current state, not on past states.

Moment matching is a statistical technique used to approximate a probability distribution by matching its moments.