Statistics 110: Probability
Harvard, , Prof. Joe Blitzstein
Added to favorite list
Updated On 02 Feb, 19
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.
4.1 ( 11 )
We introduce sample spaces and the naive definition of probability (well get to the non-naive definition later). To apply the naive definition, we need to be able to count. So we introduce the multiplication rule, binomial coefficients, and the sampling table (for sampling with/without replacement when order does/doesnt matter).
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.