Re: [Gretl-users] MCMC for dummies?

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.