Introduction to Probability with Mathematica, Second Edition

Kevin J. Hastings

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September 21, 2009 by Chapman and Hall/CRC
Textbook - 465 Pages - 125 B/W Illustrations
ISBN 9781420079388 - CAT# C7938
Series: Textbooks in Mathematics

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Features

  • Uses simulation to help students understand randomness and sampling concepts better
  • Covers the graphical technique of the normal quantile plot
  • Harnesses the interactive aspects of Mathematica to highlight the visual appeal of examples and produce interesting animations
  • Includes a CD-ROM that contains a Mathematica notebook for each section in the book, along with the KnoxProb6`Utilities`and KnoxProb7`Utilities` packages for users of Mathematica 6.0 and 7.0

Solutions manual available for qualifying instructors

Summary

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

  • Expanded section on Markov chains that includes a study of absorbing chains
  • New sections on order statistics, transformations of multivariate normal random variables, and Brownian motion
  • More example data of the normal distribution
  • More attention on conditional expectation, which has become significant in financial mathematics
  • Additional problems from Actuarial Exam P
  • New appendix that gives a basic introduction to Mathematica
  • New examples, exercises, and data sets, particularly on the bivariate normal distribution
  • New visualization and animation features from Mathematica 7.0
  • Updated Mathematica notebooks on the CD-ROM

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.

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