发表于2024-12-25
Bayesian Computation with R 2024 pdf epub mobi 电子书
感觉超级好的textbook,虽然一直不习惯R,当时还是把书上的code跑了过半,感觉对理解bayesian超级有帮助。不像其他学科,初学bayesian应该一开始就和computer结合,不然真的很没趣。这本书没太多理论,提供大量操作,循序渐进,由简单到复杂,初学bayesian如果能结合这本书一起...
评分感觉超级好的textbook,虽然一直不习惯R,当时还是把书上的code跑了过半,感觉对理解bayesian超级有帮助。不像其他学科,初学bayesian应该一开始就和computer结合,不然真的很没趣。这本书没太多理论,提供大量操作,循序渐进,由简单到复杂,初学bayesian如果能结合这本书一起...
评分感觉超级好的textbook,虽然一直不习惯R,当时还是把书上的code跑了过半,感觉对理解bayesian超级有帮助。不像其他学科,初学bayesian应该一开始就和computer结合,不然真的很没趣。这本书没太多理论,提供大量操作,循序渐进,由简单到复杂,初学bayesian如果能结合这本书一起...
评分作者有点强推自己写的R包了,对bayesian的理论思想讲的不够清楚,适合有一定理论基础的同学看,学习如何实现MCMC,推荐先看Bayesian data analysis。 其实bayesian相比frequentist理论上要简单的多,无论是估计,检验,还是回归,无非就是先验,likelihood,后验的套路。
评分感觉超级好的textbook,虽然一直不习惯R,当时还是把书上的code跑了过半,感觉对理解bayesian超级有帮助。不像其他学科,初学bayesian应该一开始就和computer结合,不然真的很没趣。这本书没太多理论,提供大量操作,循序渐进,由简单到复杂,初学bayesian如果能结合这本书一起...
图书标签: 贝叶斯 统计 数据分析 Stats Statistics R Bayesian
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner's g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.
Bayesian Computation with R 2024 pdf epub mobi 电子书