ABSTRACT

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional

chapter 1|40 pages

Probability and Counting

chapter 2|50 pages

Conditional Probability

chapter 3|46 pages

Random Variables and their Distributions

chapter 4|58 pages

Expectation

chapter 5|48 pages

Continuous Random Variables

chapter 6|34 pages

Moments

chapter 7|62 pages

Joint Distributions

chapter 8|44 pages

Transformations

chapter 9|38 pages

Conditional Expectation

chapter 10|38 pages

Inequalities and Limit Theorems

chapter 11|36 pages

Markov Chains

chapter 12|24 pages

Markov Chain Monte Carlo

chapter 13|22 pages

Poisson Processes

chapter |20 pages

A Math

chapter |6 pages

B R

chapter C|2 pages

C Table of distributions