还是讨论以下使用心得较好

谁有这方面的心得体会

楼主能介绍一下内容吗

Chapter 9 Summary

Foundations * Subjective Probability * Coherence * Decision Theory * Complete Class Theorem * Large Sample Theory

Beta–Binomial & Conjugate Normal Models * Preliminaries – Binomial Distribution – Beta Distribution – Normal Distribution – Gamma and Inverted Gamma Distributions – T–Distribution * Bayesian Inference – Joint Distribution – Marginal Distribution – Posterior Distribution – Predictive Distribution

336 CHAPTER 9. SUMMARY Linear Regression * Preliminaries – Multivariate Normal Distribution – Gamma and Inverted Gamma Distributions * Bayesian Inference – Full Conditionals – MCMC * Slice Sampling * Autocorrelated Errors

337 Multivariate Regression * Preliminaries – Matrix Facts – Matrix Normal Distribution – Wishart and Inverted Wishart Distributions * Bayesian Inference – Full Conditionals – MCMC

338 CHAPTER 9. SUMMARY Hierarchical Bayes Regression: Interaction Model * Preliminaries – Application of multiple and multivariate regression * Bayesian Inference – Within & Between Models – Full Conditionals

Hierarchical Bayes Regression: Mixture Model * Preliminaries – Multinomial Distribution – Dirichlet Distribution – Mixture Distributions * Bayesian Inference – Latent Variables

340 CHAPTER 9. SUMMARY Probit Model * Preliminaries – Random Utility Model * Bayesian Inference – Latent Variables

341 Logit Model * Preliminaries – Extreme Value Distribution * Bayesian Inference – Hastings–Metropolis Algorithm

342 CHAPTER 9. SUMMARY Conclusion * Good models include all major sources of uncertainty and variation. * Bayesian inference explicitly account for these sources. * MCMC has proven to be a flexible method of analyzing complex models. * This course has presented the basic framework. * As the complexity of your problems increase, you will want to go beyond the basics.

里面的说明书如果是中文的就好了

不过对于多数研究已经足够了

mcmc每次都用matlab编,烦死了,不知这个能解决问题否

那你那里是不是有相关的matlab程序啊

能不能给我发一下啊

我现在在作论文急用啊

先谢谢你了

wyp2u@yahoo.com.cn

thanks

等有钱买呀,好东西

好贵啊

太

贵！