INTRODUCTION TO PROBABILITY
Discover practical models and real-world applications of multivariate
models useful in engineering, business, and related disciplines
In Introduction to Probability: Multivariate Models and Applications,
a team of distinguished researchers delivers a comprehensive exploration
of the concepts, methods, and results in multivariate distributions and
models. Intended for use in a second course in probability, the material
is largely self-contained, with some knowledge of basic probability
theory and univariate distributions as the only prerequisite.
This textbook is intended as the sequel to Introduction to Probability:
Models and Applications. Each chapter begins with a brief historical
account of some of the pioneers in probability who made significant
contributions to the field. It goes on to describe and explain a
critical concept or method in multivariate models and closes with two
collections of exercises designed to test basic and advanced
understanding of the theory.
A wide range of topics are covered, including joint distributions for
two or more random variables, independence of two or more variables,
transformations of variables, covariance and correlation, a presentation
of the most important multivariate distributions, generating functions
and limit theorems. This important text:
- Includes classroom-tested problems and solutions to probability
exercises
- Highlights real-world exercises designed to make clear the concepts
presented
- Uses Mathematica software to illustrate the text's computer exercises
- Features applications representing worldwide situations and processes
- Offers two types of self-assessment exercises at the end of each
chapter, so that students may review the material in that chapter and
monitor their progress
Perfect for students majoring in statistics, engineering, business,
psychology, operations research and mathematics taking a second course
in probability, Introduction to Probability: Multivariate Models and
Applications is also an indispensable resource for anyone who is
required to use multivariate distributions to model the uncertainty
associated with random phenomena.
