Exclusive web offer for individuals. Print titles only. Terms & Conditions may apply.
Chapman and Hall/CRC
465 pages | 125 B/W Illus.
Updated to conform to Mathematica® 7.0, Introduction to Probability with Mathematica®, Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanying CD-ROM offers instructors the option of creating class notes, demonstrations, and projects.
New to the Second Edition
After covering topics in discrete probability, the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions, including normal, bivariate normal, gamma, and chi-square distributions. The author goes on to examine the history of probability, the laws of large numbers, and the central limit theorem. The final chapter explores stochastic processes and applications, ideal for students in operations research and finance.
If you own the first edition, you will be very pleased with the second edition. It is more complete, better organized, and even more well-presented. If you don’t own the first edition, and are looking for an effective tool for conveying probabilistic concepts, Hastings’ book should certainly be one you consider.
—Jane L. Harvill, The American Statistician, November 2011
Introduction to Probability with Mathematica adds computational exercises to the traditional undergraduate probability curriculum without cutting out theory. … a good textbook for a class with a strong emphasis on hands-on experience with probability. … One interesting feature of the book is that each set of exercises includes a few problems taken from actuarial exams. No doubt this will comfort students who are taking a probability course in hopes that it will prepare them for an actuarial exam. Another interesting feature is the discussion of the Central Limit Theorem. The book goes into an interesting discussion of the history of the theorem … .
—MAA Reviews, December 2009
Discrete Probability
The Cast of Characters
Properties of Probability
Simulation
Random Sampling
Conditional Probability
Independence
Discrete Distributions
Discrete Random Variables, Distributions, and Expectations
Bernoulli and Binomial Random Variables
Geometric and Negative Binomial Random Variables
Poisson Distribution
Joint, Marginal, and Conditional Distributions
More on Expectation
Continuous Probability
From the Finite to the (Very) Infinite
Continuous Random Variables and Distributions
Continuous Expectation
Continuous Distributions
The Normal Distribution
Bivariate Normal Distribution
New Random Variables from Old
Order Statistics
Gamma Distributions
Chi-Square, Student’s t, and F-Distributions
Transformations of Normal Random Variables
Asymptotic Theory
Strong and Weak Laws of Large Numbers
Central Limit Theorem
Stochastic Processes and Applications
Markov Chains
Poisson Processes
Queues
Brownian Motion
Financial Mathematics
Appendix
Introduction to Mathematica
Glossary of Mathematica Commands for Probability
Short Answers to Selected Exercises
References
Index
