Mailund on the Internet

I often use Markov Chain Monte Carlo (MCMC) methods in my research, but I still treat it a bit like magic. Sometimes it works great and sometimes getting it to mix or converge in reasonable time is just near impossible. The papers and textbooks I’ve read on the topic more often than not teaches me tricks that work on real numbers or vectors in Euclidian space, but the typical setting for me is a discrete state space (or a mix of continuous and discrete parameters) and I cannot find much in the literature to help me out with that.

Just something as simple as checking convergence of a chain, or estimating the effective sample size, is giving me problems.

Just now I saw a tool that could have helped me earlier, had it existed at the time:

AWTY (are we there yet?): a system for graphical expolration of MCMC convergence in Bayesian phylogenetics
Nylander et al.
Bioinformatics 2008 24(4):581-583; doi:10.1093/bioinformatics/btm388

Of course, I would be happier with an R package or Python module than what looks like an unholy mix of scripts, but beggars can’t be choosers.