A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!
评分标题都说了,贝叶斯统计方法的第一堂课。如果有一定统计基础,又想学贝叶斯统计,我觉得这本书作为入门书不错。比Beyesian Data Analysis可容易多了。
评分有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!
评分读这本书之前应该读一本《Statistical Inference》或者《Probability and Statistics》这样的书,否则会被那些beta函数、gamma函数搞晕。 不过这本书似乎更偏重于思想,而不是数学推导。
评分有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!有谁能证明习题3.6?谢谢!
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评分这本书真的很好,入门级,公式推导一步步很扎实!推荐给石乐志人群!
评分Bayes一周快速入门,太友好了!
评分简单明了,适合自学
评分和Albert以及Gelman三本互相呼应。
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