Douglas Bates <[EMAIL PROTECTED]> writes:
> > Of course, Monte Carlo p-values have their problems, but the world > > is not perfect.... > > Another approach is to use mcmcsamp to derive a sample from the > posterior distribution of the parameters using Markov Chain Monte > Carlo sampling. If you are interested in intervals rather than > p-values the HPDinterval function from the coda package can create > those. > We (Søren and I) actually had a look at that, and it seems not to solve the problem. Rather, mcmcsamp tends to reproduce the Wald style inference (infinite DF) if you use a suitably vague prior. It's a bit hard to understand clearly, but I think the crux is that any Bayes inference only depends on data through the likelihood function. The distribution of the likelihood never enters (the hardcore Bayesian of course won't care). However, the nature of DF corrections is that the LRT does not have its asymptotic distribution, and mcmc has no way of picking that up. -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
