dagger†: a probabilistic programming library
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.
Contributing
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.
Trivia
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)