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PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.
statistical-analysis bayesian-inference mcmc variational-inference theano probabilistic-programming bayesianstan - Stan development repository (home page is linked below)
There are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line. There are separate repositories in the stan-dev GitHub organization for the interfaces, higher-level libraries and lower-level libraries.
stan bayesian-inference bayesian bayesian-methods bayesian-statistics bayesian-data-analysispyro - Deep universal probabilistic programming with Python and PyTorch
Pyro was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at the Broad Institute. In 2019, Pyro became a project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware. For more information about the high level motivation for Pyro, check out our launch blog post. For additional blog posts, check out work on experimental design and time-to-event modeling in Pyro.
machine-learning pytorch probabilistic-programming bayesian bayesian-inference variational-inference probabilistic-modelingclassifier - [UNMAINTAINED] Bayesian classifier with Redis backend
Deprecation notice: This library is no longer actively maintained. Try the natural classifier. It doesn't have a Redis backend, but otherwise works even better. The first argument to train() can be a string of text or an array of words, the second argument can be any category name you want.
bayesian classifier machine-learningBDA_py_demos - Bayesian Data Analysis demos for Python
to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).
bayesian-data-analysis bayesian-inference bayesian mcmc stanorbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood
Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.
time-series orbit regression forecast forecasting probabilistic-programming bayesian stan arima probabilistic pyro changepoint pystan exponential-smoothingDclib - Portable C++ library
dlib is a library for developing portable applications dealing with networking, threads, graphical interfaces, data structures, linear algebra, machine learning, XML and text parsing, numerical optimization, Bayesian nets, data compression routines, linked lists, binary search trees, linear algebra and matrix utilities, machine learning algorithms, and many other general utilities.
cpp-utilities-library library algorithms compression thread bayesian machine-learning xml-parserPyro - Deep universal probabilistic programming with Python and PyTorch
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.
pytorch machine-learning bayesian webppl inference probabilistic-programming probabilistic-graphical-models bayesian-inference variational-inference ubersklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API
Python package for Bayesian Machine Learning with scikit-learn API
bayesian-machine-learning machine-learning scikit-learn bayesianbayadera - High-performance Bayesian Data Analysis on the GPU in Clojure
A Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.
bayesian-inference bayesian-data-analysis gpu-computing gpu-acceleration statistics machine-learning clojure-library bayesian opencl cuda high-performance-computing gpu mcmc markov-chain-monte-carlobambi - BAyesian Model-Building Interface (BAMBI) in Python.
Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the PyMC3 probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach.Bambi requires a working Python interpreter (either 2.7+ or 3+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.
bayesiangelato - Bayesian dessert for Lasagne
Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the best ways to deal with uncertainty, overfitting but still having good performance. Gelato will help to use bayes for neural networks. Library heavily relies on Theano, Lasagne and PyMC3.I use generic approach for decorating all Lasagne at once. Thus, for using Gelato you need to replace import statements for layers only. For constructing a network you need to be the in pm.Model context environment.
bayesian-inference lasagne uncertainty variational-inference gelato neural-network theano deep-learning bayesiannipymc - Bayesian mixed-effects modeling of fMRI data in Python
NiPyMC requires a working Python interpreter (either 2.7+ or 3+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.Once both packages are installed, you should be ready to fit models with NiPyMC.
bayesianbeat - Bayesian Earthquake Analysis Tool
This repository is still beta version and under development! There might be future changes in the API that cause previous versions to break.An alternative: The geodetic data may be saved using the package "pickle" as a file "geodetic_data.pkl" containing a list of "GeodeticTarget", especially "CompoundGPS" or "DiffIFG" objects. Please see the heart.py module for specifics.
geodesy seismology waveform-inversion earthquakes bayesian-inference bayesiansampled - Decorator for PyMC3
Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative model from using the model.
bayesiannaive-apl - Naive Bayesian Classifier written in APL
This is a simple naive bayesian classifier to gain independent probabilistic assumptions on test input. The classifier requires precisely 2 groups with training data. This is just a fun side project I did over the weekend, but any contributions would be fantastic.This example shows the classifier displaying the independent probabilistic assumptions on whether given text aligns with keywords of two different animals: cat or dog.
machine-learning bayesian bayes-classifier apinaive-bayes-classifier - yet another general purpose naive bayesian classifier.
yet another general purpose Naive Bayesian classifier. Note: Definitely you will need much more training data than the amount in the above example. Really, a few lines of text like in the example is out of the question to be sufficient training set.
bayes-classifier classifier bayesian machine-learningbayesian-bandit.js - Bayesian bandit implementation for Node and the browser.
This is an adaptation of the Bayesian Bandit code from Probabilistic Programming and Bayesian Methods for Hackers, specifically d3bandits.js. The code has been rewritten to be more idiomatic and also usable as a browser script or npm package. Additionally, unit tests are included.
machine learning bayes bayesian inference multi-armed n-armed armed bandit reinforcement statisticsLumenize - Illuminating the forest AND the trees in your data
Copyright (c) 2009-2013, Lawrence S. Maccherone, Jr. Illuminating the forest AND the trees in your data.
charting chart infographics software-engineering date time olap business-intelligence bi statistics bayes bayesian bayesian-classifier histogramDynamicHMC.jl - Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods.
Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods. This package implements a modern version of the “No-U-turn sampler” in the Julia language, mostly as described in Betancourt (2017), with some tweaks.
julia-language hybrid-monte-carlo bayesian-inference bayesian-methods bayesian hamiltonian-monte-carlo julia bayesian-statistics
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