Readings for a two-week segment on Bayesian modeling?

November 21, 2012
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Michael Landy writes:

I’m in Psych and Center for Neural Science and I’m teaching a doctoral course this term in methods in psychophysics (never mind the details) at the tail end of which I’m planning on at least 2 lectures on Bayesian parameter estimation and Bayesian model comparison. So far, all the readings I have are a bit too obscure and either glancing (bits of machine-learning books: Bishop, MacKay) or too low-level. The only useful reference I’ve got is an application of these methods (a methods article of mine in a Neuroscience Methods journal). The idea is to give them a decent idea of both estimation (Jeffries priors, marginals of the posterior over the parameters) and model comparison (cross-validation, AIC, BIC, full-blown Bayesian model posterior comparisons, Bayes factor, Occam factor, blah blah blah).

So: have you any suggestions for articles or chapters that might be suitable (yes, I’m aware you have an entire book that’s obviously relevant)? In the class topic (psychophysics), the data being modeled are typically choice data (binomial data), but my methods paper happens to be on data from measuring movement errors (continuous data), not that any of that matters.

My reply:

This is a personal view but I think BIC, Bayes factor, Occam factor, etc are bogus. I recommend you (and your students) take a look at my 1995 article with Rubin in Sociological Methodology (if you go to my home page, go to published papers, and search, you’ll find that paper) for a thorough discussion of what we hate about this. I also think Jeffreys priors are a waste of time. I wouldn’t spend one moment on that in your course if I were you. Regarding choice models, you could take a look at the section on choice models in chapter 6 of my book with Jennifer Hill. I also have a paper in Technometrics, Multilevel modeling: What it can and cannot do. Regarding the topic of model checking, you could take a look at chapter 6 of Bayesian Data Analysis.



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