csharp-probability-monad
Bayesian modelling library
A monadic probabilistic programming framework for Bayesian modelling and inference in C#.
A probabilistic programming framework for C#
96 stars
7 watching
7 forks
Language: C#
last commit: about 5 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
rlouf/mcx | Tools and methods for Bayesian deep learning using probabilistic programming. | 325 |
tweag/monad-bayes | A library that enables probabilistic programming in Haskell. | 409 |
jtassarotti/coq-proba | A Coq-based probability theory library providing results and definitions for discrete probability, measure theory, and probabilistic choice monads. | 49 |
jbrukh/bayesian | Naive Bayesian classification library for Go. | 805 |
camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers | An introduction to Bayesian methods and probabilistic programming for software developers | 26,798 |
joshuaalbert/jaxns | A probabilistic programming framework for Bayesian inference and model comparison using nested sampling and JAX. | 147 |
probcomp/bayeslite | A database system that allows querying probabilistic implications of data using Bayesian inference. | 923 |
probcomp/bayesdb | A system for querying probable implications of data using Bayesian inference and a custom query language. | 891 |
google/edward2 | A tool for writing probabilistic models and manipulating their computation | 679 |
maxsklar/bayespy | A Python library for Bayesian inference and multinomial mixture modeling | 108 |
blackjax-devs/blackjax | A Bayesian inference library designed to provide modular and customizable samplers for probabilistic programming | 846 |
michaelchughes/npbayeshmm | Software toolbox for Bayesian inference on sequential data using Markov chain Monte Carlo methods. | 78 |
amazaspshumik/sklearn-bayes | A collection of Python packages implementing Bayesian machine learning algorithms with scikit-learn API | 514 |
sisl/bayesnets.jl | A Julia package for representing and working with probabilistic graphical models. | 218 |
oasic/nbayes | A Ruby implementation of Naive Bayes for probability estimation and classification tasks | 153 |