Counting: Permutations, Combinations, and Poker Hands

Related lectures (30)

Page 2 of 3

Explains the importance of moments in measuring distribution properties, such as expectation and variance.

Covers constructing real numbers through decimal expansion and the concept of discrete and continuous random variables.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores different modes of convergence for random variables.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Explains random variables, distributions, and Bernoulli trials with coin flip examples.