Statistics 110 -- Introduction to Probability
Course Description: A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, bounds, Normal, t, Binomial, Negative Binomial, Poisson, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.

This class is at the Undergraduate level
Instructor: Joseph K. Blitzstein
Prerequisites: Mathematics 19a or equivalent or above required (may be taken concurrently), Mathematics 19b or equivalent or above recommended.

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