The dagger library for probabilistic programming in OCaml. Currently implements:

• Single-site Metropolis-Hastings, a.k.a. lightweight Metropolis-Hastings
• Incrementalized single-site MH, similar to that implemented in Hakaru10
• Sequential Monte-Carlo, with systematic and stratified resampling plus facilities for defining custom resampling strategies.

The main package is prbnmcn-dagger. Packages prbnmcn-dagger-gsl and prbnmcn-dagger-stats provide distributions implemented respectively through the GSL (GPL-licensed) and prbnmcn-stats (MIT-licensed).

Look no further for the documentation and the examples it contains.

Some more examples are made available in the examples subdirectory. For now, you'll find:

• an implementation of a 2d ising model and a toy study of its behaviour around its critical temperature
• a basic kalman filter example
• an experiment on forecasting wind power production using an ad-hoc Kalman filter
• an implementation of the algorithm described in Adaptive approximate Bayesian computation by Beaumont et al.

Contributions and issue reports are welcome. Development currently happen on https://gitlab.com/igarnier/monorepo/ but I can take care of cherry-picking pull requests submitted here.

The name dagger refers to two things:

• a good mathematical framework for giving a semantics to probabilistic programming is a certain dagger category of Markov kernels, see eg this or that paper; Bayesian inversion corresponds to a particular symmetry of a mathematical structure and this symmetry is denoted using the † symbol.
• the underlying representation of the probabilistic model when using the incrementalized backend is as a directed acyclic graph (ie a DAG, which sounds exactly like the French translation of dagger)