Review article on Bayesian inference in physics

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I haven't read it yet, but the abstract reminded me of something I was thinking about recently: Share Likelihoods, Not Posterior Beliefs really, *really* needs to get published somewhere mainstream.

Glad to see that Hamiltonian / Hybrid Monte Carlo is gaining more interest, though I guess it's supposed to have had more interest in physics than statistics for a while. Given how much better these algorithms scale, I think they should get a lot more attention. The description of Parallel tempering was pretty good, I had heard it described before but didn't get it. I get a sense for why it's exciting to people. The section on Reversible Jump MCMC made me realize I don't understand model selection problems very well at all.

A nice article just appeared in Reviews of Modern Physics. It offers a brief coverage of the fundamentals of Bayesian probability theory, the practical numerical techniques, a diverse collection of real-world examples of applications of Bayesian methods to data analysis, and even a section on Bayesian experimental design. The PDF is available here.

The abstract: