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There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Author website: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/

-Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment

Doing Bayesian Data Analysis 2025 pdf epub mobi 電子書下載

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Doing Bayesian Data Analysis 2025 pdf epub mobi 電子書下載

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Chapter 1 - 3 R basics getwd() Change work directory: Session -> Set Work directory List the files in the working directory: dir() ls() source("mycode.r") myfunction <- function() { x<-rsnorm(100) mean(x) } second <- function(x) { x+rnorm(length...

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力薦給缺乏足夠統計基礎（如非stats專業 bachelor）的初學者。Dr. Kruschke自己有blog，會經常在上麵迴答（書中提到的）問題和深入討論。使用的軟件是R，主要算法集中在Gibbs Sampler方麵，對HMC沒有太多介紹。另讀完之後想要完全獨立做Bayesian，還是要迴去吃下Gelman的書。

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出版者:Academic Press

作者:John K. Kruschke

出品人:

頁數:672

譯者:

出版時間:2010-11-25

價格:GBP 64.48

裝幀:Hardcover

isbn號碼:9780123814852

叢書系列: