rhine-bayes: monad-bayes backend for Rhine

This is a package candidate release! Here you can preview how this package release will appear once published to the main package index (which can be accomplished via the 'maintain' link below). Please note that once a package has been published to the main package index it cannot be undone! Please consult the package uploading documentation for more information.

[maintain] [Publish]

Warnings:

These packages miss upper bounds: - log-domain - mmorph - monad-bayes - rhine-gloss - time - transformers Please add them, using `cabal gen-bounds` for suggestions. For more information see: https://pvp.haskell.org/

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.

[Skip to Readme]

Properties

Versions	0.8.1.1, 0.9, 1.0, 1.0, 1.1, 1.2, 1.3, 1.4, 1.4.0.1, 1.5
Change log	ChangeLog.md
Dependencies	base (>=4.11 && <4.18), dunai (>=0.9 && <0.10), log-domain (>=0.12), mmorph, monad-bayes (>=1.1.0), rhine (==1.0), rhine-bayes, rhine-gloss, 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 v1.0)
Uploaded	by turion at 2023-07-21T10:08:29Z

Modules

[Index] [Quick Jump]

FRP
- Rhine
  - FRP.Rhine.Bayes

Flags

Manual Flags

Name	Description	Default
dev	Enable warnings as errors. Active on ci.	Disabled

Use -f <flag> to enable a flag, or -f -<flag> to disable that flag. More info

Downloads

rhine-bayes-1.0.tar.gz [browse] (Cabal source package)
Package description (as included in the package)

Maintainer's Corner

Package maintainers

turion

For package maintainers and hackage trustees

edit package information

Readme for rhine-bayes-1.0

[back to package description]

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.