Introduction to Stochastic Processes with RAn introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical software R, makes theoretical results come alive with practical, hands-on demonstrations. Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers’ problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features:
Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic. |
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Conteúdo
1 | |
First Steps | 40 |
Chapter 3 Markov Chains for the Long Term | 76 |
Chapter 4 Branching Processes | 158 |
Chapter 5 Markov Chain Monte Carlo | 181 |
Chapter 6 Poisson Process | 223 |
Chapter 7 ContinuousTime Markov Chains | 265 |
Chapter 8 Brownian Motion | 320 |
Appendix A Getting Started with R | 400 |
Appendix B Probability Review | 421 |
Appendix C Summary of Common Probability Distributions | 443 |
Appendix D Matrix Algebra Review | 445 |
Answers to Selected OddNumbered Exercises | 455 |
470 | |
475 | |
EULA | 481 |