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Introduction to Stochastic Processes with R
An 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
More than 200 examples and 600 end-of-chapter exercises A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black–Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus Introductions to mathematics as needed in order to suit readers at many mathematical levels A companion web site that includes relevant data files as well as all R code and scripts used throughout the book 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.
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
More than 200 examples and 600 end-of-chapter exercises A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black–Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus Introductions to mathematics as needed in order to suit readers at many mathematical levels A companion web site that includes relevant data files as well as all R code and scripts used throughout the book 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.
- GenresMathematicsNonfiction
435 pages, Kindle Edition
Published March 29, 2016
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Displaying 1 - 2 of 2 reviews
May 19, 2021
I was glad to pick this book when first studying stochastic processes. Even though I self-studied this topic, my experience went so smoothly and joyfully. The book is well-structured and full of fascinating examples. Applications, such as Black-Scholes formula for pricing options and MCMC in Darwin's finches, were presented along with comprehensible proofs, keeping me excited throughout the time I was reading.
November 28, 2020
It was an enjoyment to go through the book. The topic itself is not very challenging and book presents most of the material quite straightforwardly.
I find the Gibbs sampler in chapter 5 still a bit confusing, and I wonder whether there are better examples to illustrate the algorithm mathematically. Also I would also love to have a solution manual for the exercises.
I find the Gibbs sampler in chapter 5 still a bit confusing, and I wonder whether there are better examples to illustrate the algorithm mathematically. Also I would also love to have a solution manual for the exercises.
Displaying 1 - 2 of 2 reviews