pebl
Bayesian network learner
A Python library and command line application for learning Bayesian network structures from observational data.
Python Environment for Bayesian Learning
104 stars
8 watching
21 forks
Language: Python
last commit: about 13 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
amazaspshumik/sklearn-bayes | A collection of Python packages implementing Bayesian machine learning algorithms with scikit-learn API | 514 |
robinthibaut/skbel | A Python framework for Bayesian inference and regression using Gaussian processes. | 22 |
arviz-devs/arviz | A Python package for exploring and analyzing Bayesian models in data science | 1,613 |
maxsklar/bayespy | A Python library for Bayesian inference and multinomial mixture modeling | 108 |
mcleonard/sampyl | An MCMC sampler library for Bayesian estimation using Python | 330 |
mattcunningham/naive-apl | A naive Bayesian classifier implemented in APL | 22 |
ctallec/pyvarinf | A Python package facilitating Bayesian Deep Learning methods with Variational Inference for PyTorch | 359 |
bayesnet/bnt | Provides a set of tools and algorithms for Bayesian networks in MATLAB | 468 |
camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers | An introduction to Bayesian methods and probabilistic programming for software developers | 26,820 |
sisl/bayesnets.jl | A Julia package for representing and working with probabilistic graphical models. | 218 |
jbrukh/bayesian | A Go library implementing Naive Bayesian classification with optional term frequency-inverse document frequency (TF-IDF) calculations. | 805 |
eaigner/shield | A flexible Bayesian text classifier with backend storage support | 158 |
mattjj/pyhsmm | A Python library providing approximate inference methods for Bayesian Hidden Markov Models and their extensions. | 550 |
alvations/bayesline | A software framework implementing a multivariate Bayesian classifier for language identification | 0 |
albermax/innvestigate | A toolbox to help understand neural networks' predictions by providing different analysis methods and a common interface. | 1,268 |