x

# Statistics 110: Probability

Harvard, , Prof. Joe Blitzstein

Updated On 02 Feb, 19

##### Overview

This course is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. The ideas and methods are useful in statistics, science, engineering, economics, finance, and everyday life. Topics include the following. Basics: sample spaces and events, conditioning, Bayes' Theorem. Random variables and their distributions: distributions, moment generating functions, expectation, variance, covariance, correlation, conditional expectation. Univariate distributions: Normal, t, Binomial, Negative Binomial, Poisson, Beta, Gamma. Multivariate distributions: joint, conditional, and marginal distributions, independence, transformations, Multinomial, Multivariate Normal. Limit theorems: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, reversibility, convergence.

## Lecture 6: Lecture 8: Random Variables and Their Distributions | Statistics 110

4.1 ( 11 )

###### Lecture Details

Much of this course is about random variables and their distributions. The relationship between a random variable and its distribution can seem subtle but it is essential! We discuss distributions, cumulative distribution functions (CDFs), probability mass functions (PMFs), and the Hypergeometric distribution.

0 Ratings
55%
30%
10%
3%
2%