package prbnmcn-dagger-stats

  1. Overview
  2. Docs
Probabilistic programming library: prbnmcn-stats-based samplers

Install

Dune Dependency

Authors

Maintainers

Sources

0.0.4.tar.gz
md5=4a41b2cea5eda7fbbe93a009b8dcee9a
sha512=cd747365c7cbdbc3c4c3a7fe39daea1ffbf7dd38a047f1eff01f6dc0b5a71635219f911b35110a6cf4935fc92189dcb8012ed6ba5718f74bb291142fb18878db

README.md.html

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)

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