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
具體描述
讀後感
力荐给缺乏足够统计基础（如非stats专业 bachelor）的初学者。Dr. Kruschke自己有blog，会经常在上面回答（书中提到的）问题和深入讨论。 使用的软件是R，主要算法集中在Gibbs Sampler方面，对HMC没有太多介绍。 另读完之后想要完全独立做Bayesian，还是要回去吃下Gelman的书。
評分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...
評分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...
評分力荐给缺乏足够统计基础（如非stats专业 bachelor）的初学者。Dr. Kruschke自己有blog，会经常在上面回答（书中提到的）问题和深入讨论。 使用的软件是R，主要算法集中在Gibbs Sampler方面，对HMC没有太多介绍。 另读完之后想要完全独立做Bayesian，还是要回去吃下Gelman的书。
評分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...
用戶評價
偏傳統統計
评分貝葉斯極端主義者。講解方式跟封麵一樣另類， 喜歡的人喜歡， 但很多人應該不能接受， 我就是不太能接受的那一類...
评分this book has so many strange terms... perhaps the author is a psychologist.
评分After a year of persistent reading, I finally finished this grimoire of statistical dark magic. Best statistical book, ever. Reading it was a testimony of my two major sources of motivation: stubbornness and hatred of incompetence.
评分:無
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