This book is a comprehensive guide to multivariate probability for students who have an elementary knowledge of probability and are ready to move on to more advanced concepts
Topics covered include:
A review of basic probability theory, including core ideas about random variables
Bivariate distributions and the general theory of random vectors
Relationships between random variables
Normal linear model and multivariate sampling distributions
Generating functions and convergence.
Each section is illustrated with numerous examples. Multivariate probability deliberately avoids a measure-theoretic approach in order to make these complex concepts easily accessible to a broad readership. Attention is restricted to discrete and (absolutely) continuous random variables. Although proofs are given of all the main results, this book is primarily intended to provide readers with the tools they require to build appropriate probability models for real-life situations. The usefulness of simulation in this respect is emphasized throughout the book.
