rhine-bayes: monad-bayes backend for Rhine

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This package provides a backend to the monad-bayes library, enabling you to write stochastic processes as signal functions, and performing online machine learning on them.

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FRP
- Rhine
  - FRP.Rhine.Bayes

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rhine-bayes-1.5.tar.gz [browse] (Cabal source package)
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turion

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Candidates

1.0, 1.1, 1.4

Versions [RSS]	0.8.1.1, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.4.0.1, 1.5
Change log	ChangeLog.md
Dependencies	automaton, base (>=4.16 && <4.21), log-domain (>=0.12), mmorph (>=1.2 && <1.3), monad-bayes (>=1.3 && <1.4), rhine (>=1.5 && <1.6), rhine-bayes, rhine-gloss (>=1.5 && <1.6), time, transformers (>=0.5) [details]
License	BSD-3-Clause
Author	Manuel Bärenz
Maintainer	programming@manuelbaerenz.de
Category	FRP
Source repo	head: git clone git@github.com:turion/rhine.git this: git clone git@github.com:turion/rhine.git(tag v1.5)
Uploaded	by turion at 2024-11-12T19:29:45Z
Distributions	LTSHaskell:1.5
Executables	rhine-bayes-gloss
Downloads	389 total (16 in the last 30 days)
Rating	(no votes yet) [estimated by Bayesian average]
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Status	Docs available [build log] Last success reported on 2024-11-12 [all 1 reports]

Readme for rhine-bayes-1.5

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README

This package connects rhine to the monad-bayes library for probabilistic programming and inference. It provides:

Some standard stochastic processes such as Brownian Motion and Levý processes
A particle filter inference method called Sequential Monte Carlo

This allows you to do interactive probabilistic (i.e. involving randomness) programs, and at the same time perform online inference, or realtime machine learning.

An example for this is given in rhine-bayes/app/Main.hs, where inference is performed both on simulated values as well as external input given by the user.

To read more, have a look at https://www.tweag.io/blog/2023-10-12-rhine-bayes/.