Table of Contents Last updated Save as PDF Page ID9821 1: What is Probability?1.1: Sample Spaces and Events1.2: Probability Measures2: Computing Probabilities2.1: Equally Likely Outcomes and Counting Techniques (Combinatorics)2.2: Conditional Probability and Bayes' Rule2.3: Independent Events3: Discrete Random Variables3.1: Introduction to Random Variables3.2: Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs) for Discrete Random Variables3.3: Bernoulli and Binomial Distributions3.4: Hypergeometric, Geometric, and Negative Binomial Distributions3.5: Poisson Distribution3.6: Expected Value of Discrete Random Variables3.7: Variance of Discrete Random Variables3.8: Moment-Generating Functions (MGFs) for Discrete Random Variables4: Continuous Random Variables4.1: Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) for Continuous Random Variables4.2: Expected Value and Variance of Continuous Random Variables4.3: Uniform Distributions4.4: Normal Distributions4.5: Exponential and Gamma Distributions4.6: Weibull Distributions4.7: Chi-Squared Distributions4.8: Beta Distributions5: Probability Distributions for Combinations of Random Variables5.1: Joint Distributions of Discrete Random Variables5.2: Joint Distributions of Continuous Random Variables5.3: Conditional Probability Distributions5.4: Finding Distributions of Functions of Continuous Random Variables5.5: Sample MeanBack MatterIndexGlossary