文件名称:Bayesianmethods
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 15.63mb
- 下载次数：
- 1次
- 提 供 者：
- termi*****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
本压缩文件详细介绍了Robert Piche博士关于贝叶斯算法的理论和他的笔记，其中文档中还包含源码程序，另附两个m文件源程序，是一个非常实用的学习及参考资料-In this course we present the basic principles of Bayesian statistics (an alternative to "orthodox" statistics). We start by learning how to estimate parameters for standard models (normal, binomial, Poisson) and then get acquainted with computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications. Advanced topics include model comparison and decision theory.
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下载文件列表
MAT-51706 Bayesian methods\BayesDental1.pdf
..........................\BayesDental2.pdf
..........................\GibbsDemo.m
..........................\lecture01.pdf
..........................\lecture02.pdf
..........................\lecture03.pdf
..........................\lecture04.pdf
..........................\lecture05.pdf
..........................\lecture06.pdf
..........................\lecture07.pdf
..........................\lecture08.pdf
..........................\lecture09.pdf
..........................\lecture10.pdf
..........................\lecture11.pdf
..........................\NewcombGibbs.m
..........................\notes01.pdf
..........................\notes02.pdf
..........................\notes03.pdf
..........................\notes04.pdf
..........................\notes05.pdf
..........................\notes06.pdf
..........................\notes07.pdf
..........................\notes08.pdf
..........................\notes09.pdf
..........................\notes10.pdf
..........................\notes11.pdf
..........................\set01.pdf
..........................\set02.pdf
..........................\set03.pdf
..........................\set04.pdf
..........................\set05.pdf
..........................\set06.pdf
..........................\set07.pdf
..........................\set08.pdf
..........................\set09.pdf
..........................\set10.pdf
..........................\set11.pdf
..........................\SI_Introduction.pdf
..........................\sol11.pdf
MAT-51706 Bayesian methods
..........................\BayesDental2.pdf
..........................\GibbsDemo.m
..........................\lecture01.pdf
..........................\lecture02.pdf
..........................\lecture03.pdf
..........................\lecture04.pdf
..........................\lecture05.pdf
..........................\lecture06.pdf
..........................\lecture07.pdf
..........................\lecture08.pdf
..........................\lecture09.pdf
..........................\lecture10.pdf
..........................\lecture11.pdf
..........................\NewcombGibbs.m
..........................\notes01.pdf
..........................\notes02.pdf
..........................\notes03.pdf
..........................\notes04.pdf
..........................\notes05.pdf
..........................\notes06.pdf
..........................\notes07.pdf
..........................\notes08.pdf
..........................\notes09.pdf
..........................\notes10.pdf
..........................\notes11.pdf
..........................\set01.pdf
..........................\set02.pdf
..........................\set03.pdf
..........................\set04.pdf
..........................\set05.pdf
..........................\set06.pdf
..........................\set07.pdf
..........................\set08.pdf
..........................\set09.pdf
..........................\set10.pdf
..........................\set11.pdf
..........................\SI_Introduction.pdf
..........................\sol11.pdf
MAT-51706 Bayesian methods