This may appear to be totally off-topic but it's not entirely so, given that we've had a "feature request" at sourceforge for a Gibbs sampler implementation. Anyway, does anyone have a recommendation for a sort of "Markov Chain Monte Carlo for dummies" -- a useful book, article or website?
I understand the principles of Monte Carlo analysis pretty well; I've read some interesting arguments in favour of a Bayesian approach in statistics (though I'm basically a frequentist); and I have some notion of what Markov chains are; but I'm having trouble putting the whole picture together. That is, if we start from some econometric problem, and we assume some relevant data are available -- and maybe we also assume that I have some prior beliefs about the problem in question that could be quantified to some extent, in some way -- how exactly could I use MCMC to arrive at "better" (in what sense?) parameter estimates, confidence intervals for these estimates, forecasts, and confidence intervals for the forecasts, than I could obtain via regular OLS, GLS, MLE, or GMM? I'm not asking people to explain this to me here, just to give any references that they have found particularly useful. Thanks. Allin Cottrell
