Developed from celebrated Harvard statistics lectures,Introduction to Probability provides essential language and toolsfor 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 application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version.
The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use storiesto uncover connections between the fundamental distributions in statistics and conditioningto reduce complicated problems to manageable pieces.
The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
Probability and Counting Why Study Probability? Sample Spaces and Pebble World Naive Definition of Probability How to Count Story Proofs Non-Naive Definition of Probability Recap R Exercises Conditional Probability The Importance of Thinking Conditionally Definition and Intuition Bayes' Rule and the Law of Total Probability Conditional Probabilities Are Probabilities Independence of Events Coherency of Bayes' Rule Conditioning as a Problem-Solving Tool Pitfalls and Paradoxes Recap R Exercises Random Variables and Their Distributions Random Variables Distributions and Probability Mass Functions Bernoulli and Binomial Hypergeometric Discrete Uniform Cumulative Distribution Functions Functions of Random Variables Independence of r.v.s Connections Between Binomial and Hypergeometric Recap R Exercises Expectation Definition of Expectation Linearity of Expectation Geometric and Negative Binomial Indicator r.v.s and the Fundamental Bridge Law of The Unconscious Statistician (LOTUS) Variance Poisson Connections Between Poisson and Binomial Using Probability and Expectation to Prove Existence Recap R Exercises Continuous Random Variables Probability Density Functions Uniform Universality of The Uniform Normal Exponential Poisson Processes Symmetry of i.i.d. Continuous r.v.s Recap R Exercises Moments Summaries of a Distribution Interpreting Moments Sample Moments Moment Generating Functions Generating Moments With MGFs Sums of Independent r.v.s Via MGFs Probability Generating Functions Recap R Exercises Joint Distributions Joint, Marginal, and Conditional 2D LOTUS Covariance and Correlation Multinomial Multivariate Normal Recap R Exercises Transformations Change of Variables Convolutions Beta Gamma Beta-Gamma Connections Order Statistics Recap R Exercises Conditional Expectation Conditional Expectation Given an Event Conditional Expectation Given an r.v. Properties of Conditional Expectation Geometric Interpretation of Conditional Expectation Conditional Variance Adam and Eve Examples Recap R Exercises Inequalities and Limit Theorems Inequalities Law of Large Numbers Central Limit Theorem Chi-Square and Student- t Recap R Exercises Markov Chains Markov Property and Transition Matrix Classification of States Stationary Distribution Reversibility Recap R Exercises Markov Chain Monte Carlo Metropolis-Hastings Gibbs Sampling Recap R Exercises Poisson Processes Poisson Processes in One Dimension Conditioning, Superposition, Thinning Poisson Processes in Multiple Dimensions Recap R Exercises Math Sets Functions Matrices Difference Equations Differential Equations Partial Derivatives Multiple Integrals Sums Pattern Recognition Common Sense and Checking Answers R Vectors Matrices Math Sampling and Simulation Plotting Programming Summary Statistics Distributions Table of Distributions Bibliography Index
