flat-mcmc Update and v1.0.0 Release

07 Apr 2016

I’ve updated my old flat-mcmc library for ensemble sampling in Haskell and have pushed out a v1.0.0 release.

History

I wrote flat-mcmc in 2012, and it was the first serious-ish size project I attempted in Haskell. It’s an implementation of Goodman & Weare’s affine invariant ensemble sampler, a Monte Carlo algorithm that works by running a Markov chain over an ensemble of particles. It’s easy to get started with (there are no tuning parameters, etc.) and is sufficiently robust for a lot of purposes. The algorithm became somewhat famous in the astrostatistics community, where some of its members implemented it via the very nice and polished Python library, emcee.

The library has become my second-most starred repo on Github, with a whopping 10 stars as of this writing (the Haskell MCMC community is pretty niche, bro). Lately someone emailed me and asked if I wouldn’t mind pushing it to Stackage, so I figured it was due for an update and gave it a little modernizing along the way.

I’m currently on sabbatical and am traveling through Vietnam; I started the rewrite in Hanoi and finished it in Saigon, so it was a kind of nice side project to do while sipping coffees and the like during downtime.

What Is It

I wrote a little summary of the library in 2012, which you can still find tucked away on my personal site. Check that out if you’d like a description of the algorithm and why you might want to use it.

Since I wrote the initial version my astrostatistics-inclined friends David Huijser and Brendon Brewer wrote a paper about some limitations they discovered when using this algorithm in high-dimensional settings. So caveat emptor, buyer beware and all that.

In general this is an extremely easy-to-use algorithm that will probably get you decent samples from arbitrary targets without tedious tuning/fiddling.

What’s New

I’ve updated and standardized the API in line with my other MCMC projects huddled around the declarative library. That means that, like the others, there are two primary ways to use the library: via an mcmc function that will print a trace to stdout, or a flat transition operator that can be used to work with chains in memory.

Regrettably you can’t use the flat transition operator with others in the declarative ecosystem as it operates over ensembles, whereas the others are single-particle algorithms.

The README over at the Github repo contains a brief usage example. If there’s some feature you’d like to see or documentation/examples you could stand to have added then don’t hestitate to ping me and I’ll be happy to whip something up.

In the meantime I’ve pushed a new version to Hackage and added the library to Stackage, so it should show up in an LTS release soon enough.

Cheers!