Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Introduction to Probability Simulation and Gibbs Sampling with R

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Eric A. Suess 作者
Springer
译者
2010-6-15 出版日期
307 页数
USD 64.95 价格
Paperback
Use R 丛书系列
9780387402734 图书编码

Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 图书标签: R  科普  模拟  数据处理  Gibbs采样   


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Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Introduction to Probability Simulation and Gibbs Sampling with R 在线电子书 图书描述

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.

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