While we're mentioning Haskell-embedded languages for probabilistic programming, there is yet another, Hakaru.

github: https://github.com/hakaru-dev/hakaru docs: https://hakaru-dev.github.io/ hackage: https://hackage.haskell.org/package/hakaru-0.4.0

It can be used either embedded or via an external syntax. It can act as a sampler, but also do code generation (to Haskell, to C, more coming), simplification (via Maple if it is installed) and disintegration (aka conditioning).

It uses quite a number of advanced Haskell features (though works in 7.8 onwards) to insure safety of the embedded language. See https://hackage.haskell.org/package/hakaru-0.4.0/docs/Language-Hakaru-Syntax... if you are curious about that.

Jacques

There are production strength PPLs like Stan and PyMC3. They aren’t Haskell but if you actually want to model something then they are my “go to” tools. There’s also LibBI which uses Sequential Monte Carlo rather than Hamiltonian Monte Carlo. Stan and LibBI are C++ with interfaces (I use the word very loosely) from e.g. R, Python, Matlab, etc. PyMC3 as you might guess is entirely Python. Both have very good communities. In Haskell there is also monad-bayes https://github.com/adscib/monad-bayes https://github.com/adscib/monad-bayes and baysig, the latter sadly not publicly available (last time I checked). If you have a hidden state model (e.g. stochastic volatility) then there is also particle filtering (aka Sequential Monte Carlo) using https://hackage.haskell.org/package/kalman-1.0.0.2 https://hackage.haskell.org/package/kalman-1.0.0.2. I just looked at the commit log for monad-bayes and it seems to use HMC these days :) and supports ADVI. I spent a bit of time at the recent Zurihac with Alexander Vershilov making some very small steps towards a symplectic integrator package which could form the basis for HMC in Haskell. Dominic Steinitz dominic@steinitz.org http://idontgetoutmuch.wordpress.com