Introduction to Probability with Mathematica

Download or Read eBook Introduction to Probability with Mathematica PDF written by Kevin J. Hastings and published by CRC Press. This book was released on 2009-09-21 with total page 467 pages. Available in PDF, EPUB and Kindle.
Introduction to Probability with Mathematica
Author :
Publisher : CRC Press
Total Pages : 467
Release :
ISBN-10 : 9781420079401
ISBN-13 : 1420079409
Rating : 4/5 (01 Downloads)

Book Synopsis Introduction to Probability with Mathematica by : Kevin J. Hastings

Book excerpt: 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 (Go to Downloads/Updates tab for link to CD files.) 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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