I expect many people have their own MCMC (Markov chain Mote Carlo) implementations for Bayesian parameter estimation (in multiple programming languages) and indeed I also have my own. However, rather than keeping my Matlab implementation to myself I’ve decided to release it as “yamm” (**Y**et **A**nother **M**atlab **M**CMC code) on github. This is obviously not the only Matlab MCMC code (see e.g. here or here for a couple of examples), and I make no claim for it being the best optimised, fastest or most efficient code (it’s very much at best beta in terms of a release), but hopefully it might prove useful to others.

In writing the code I acknowledge various code that has helped it’s development. The various proposal distributions were helped greatly by the work of Veitch & Vecchio (arXiv:0911.3820) and implementations currently in the LALinference software suite. This includes the affine invariant ensemble samplers of Goodman & Weare as also implemented in the python emcee software by Foreman-Mackey et al. (arXiv:1202.3665).

Any comments or suggestions about the software are welcome here. This code is quite similar to the Matlab Nested Sampler code described here.