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Previous replies are detailed enough, but an intuitive way of looking why $P (A) $ (ie dinominator in the Bayes theorem) is broken into two cases. It is hard to comment about what is the $P(A)$ witho …

I was thinking about CI and subjective Bayesian and I have following two questions: If a subjective (not objective) Bayesian would care if her predictions don't do well in the real world. …

Using the terminology of Profile Likelihood from the maximum likelihood estimation theory, your way of finding the $f$ may be called profile posterior distribution.

There is another camp in Bayesian world which recommends model averaging. Notable being, Dr. Raftery. Bayesian model averaging. … This website of Chris Volinksy is a comprehensive source of Bayesian model averging. Some other works are here. …

The only similarity in the two models is the general type of models they belong to, otherwise they are not similar in general as pointed out by M. Tibbits. Both these models belong the class of hiera …

Lets see if I understand Harlan's (and Srikant's) formulation correctly. $$\pi_1 \sim beta(\alpha_1,\beta_1)$$ $$\pi_2 \sim beta(\alpha_2,\beta_2)$$ Say, $\pi_1$ corresponds to the set of data for w …

A naive Bayes classifier, as the names suggests, is a simple application of Bayes' Theorem. Basically, it calculates the probabilities of quantities of interest (generally unobserved, called parameter …

Brown, Cai, and DasGupta, AS, 2002 Brown, Cai, and DasGupta, Stat Sci, 2001 I don't know if I understand you correctly, but in my knowledge the above two papers are the most cited ones recently when i …

I am looking for a good tutorial on clustering data in R using hierarchical dirichlet process (HDP) (one of the recent and popular nonparametric Bayesian methods). … There is DPpackage (IMHO, the most comprehensive of all the available ones) in R for nonparametric Bayesian analysis. …

I am assuming you want to derive: \begin{align*} P(w,d) = \sum_{c} P(c) P(d|c) P(w|c) &= P(d) \sum_{c} P(c|d) P(w|c) \end{align*} Further, this is similar to Probabilistic latent semantic indexing …

Bayesian Data Analysis. CRC Press. Section 3.6 Edit: Ringold, the behavior observed by you is consistent with the Bayesian idea. … The Bayesian Credible Interval (CI) is generally wider than the classical ones. …

You have framed your question very well. I think what you are looking for here is a case of hierarchical modeling. And you may want to model multiple layers of hierarchy (at the moment you only talk …

Fisher was trying to find a way different from both the classical statistics (of Neyman School) and the bayesian school (hence the famous adage from Savage: "Fisher wanted to make a Bayesian omelette ( … ie using CP) without breaking the Bayesian eggs"). …