这本书2014年出版，一直没看，发现2016年出版了带导读的英文版，就把它放上来了。它从贝叶斯讲到马尔科夫、吉布斯抽样、回归模型，洋洋洒洒六百多页，挺厚的一本，

配合教材Bayesian Reasoning and Machine Learning的源代码，值得收藏

贝叶斯抠图算法，使用mfc实现，直接编译可运行。 运行时先打开原图片和trimap，然后点击菜单中的抠图。

The rise of probability theory changed that. Statistical inference compels us instead to rely on Fortuna as a servant of Minerva, to use chance and uncertainty to discover reliable knowledge. All flavors of statistical inference have this motivation. But Bayesian data analysis embraces it most fully, by using the language of chance to describe the plausibility of different possibilities. There are many ways to use the term “Bayesian.” But mainly it denotes a particular interpretation of probability. In modest terms, Bayesian inference is no more than counting the numbers of ways things can happen, according to our assumptions. Things that can happen more ways are more plausible. And since probability theory is just a calculus for counting, this means that we can use probability theory as a general way to represent plausibility, whether in reference to countable events in the world or rather theoretical constructs like parameters. Once you accept this gambit, the rest follows logically. Once we have defined our assumptions, Bayesian inference forces a purely logical way of processing that information to produce inference.

贝叶斯入门参考书，贝叶斯入门参考书，贝叶斯入门参考书，贝叶斯入门参考书

贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现。

For Bayesian learning. For beginners. Easy but useful

winbugs说明和建模代码

稀疏信号处理理论、方法及应用研究进展概况 稀疏信号的表示与采样原理 稀疏信号重建算法之梯度优化方法 鲁班稀疏Bayesian优化方法 压缩感知滤波器等应用实例

贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现。