A library for probabilistic programming. It takes the form of a signature for a monadic language exposing sampling and scoring primitives together with inference algorithms implementing that signature, including:
- sequential Monte Carlo (SMC)
- single-site lightweight Metropolis-Hastings
Published: 13 Sep 2023
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-stats provide distributions implemented respectively through the GSL (GPL-licensed) and
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)