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(Ebook) Introduction to Probability with Mathematica, Second Edition by Kevin J. Hastings ISBN 9781420079388, 1420079387

  • SKU: EBN-5059138
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Instant download (eBook) Introduction to Probability with Mathematica, Second Edition after payment.
Authors:Kevin J. Hastings
Pages:465 pages.
Year:2009
Editon:2
Publisher:Chapman & Hall/CRC Solutions manual
Language:english
File Size:3.51 MB
Format:pdf
ISBNS:9781420079388, 1420079387
Categories: Ebooks

Product desciption

(Ebook) Introduction to Probability with Mathematica, Second Edition by Kevin J. Hastings ISBN 9781420079388, 1420079387

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 chainsNew sections on order statistics, transformations of multivariate normal random variables, and Brownian motionMore example data of the normal distributionMore attention on conditional expectation, which has become significant in financial mathematicsAdditional problems from Actuarial Exam PNew appendix that gives a basic introduction to MathematicaNew examples, exercises, and data sets, particularly on the bivariate normal distributionNew visualization and animation features from Mathematica 7.0Updated 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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