Introduction to Probability with Mathematica (Textbooks in Mathematics 8 2nd Revised edition)
By: Kevin J. Hastings (author)Hardback
1 - 2 weeks availability
Updated to conform to Mathematica(R) 7.0, Introduction to Probability with Mathematica(R), 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.
Kevin J. Hastings is a professor of mathematics at Knox College in Galesburg, Illinois.
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
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- ID: 9781420079388
2nd Revised edition
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