pyro - Deep universal probabilistic programming with Python and PyTorch

•    Python

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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-modeling

PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

•    Python

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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 bayesian

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view

•    Jupyter

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The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

bayesian-methods pymc mathematical-analysis jupyter-notebook data-science statistics

infer - Infer.NET is a framework for running Bayesian inference in graphical models

•    CSharp

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Infer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. One can use Infer.NET to solve many different kinds of machine learning problems - from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.

machine-learning bayesian-inference

edward - A probabilistic programming language in TensorFlow

•    Jupyter

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Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

bayesian-methods deep-learning machine-learning data-science tensorflow neural-networks statistics probabilistic-programming

orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood

•    Python

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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-smoothing

numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU

•    Python

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Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. NumPyro is a small probabilistic programming library that provides a NumPy backend for Pyro. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU. This is an alpha release under active development, so beware of brittleness, bugs, and changes to the API as the design evolves.

numpy probabilistic-programming bayesian-inference hmc pyro jax inference-algorithms

probability - Probabilistic reasoning and statistical analysis in TensorFlow

•    Jupyter

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TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation. Our probabilistic machine learning tools are structured as follows.

tensorflow bayesian-methods deep-learning machine-learning data-science neural-networks statistics probabilistic-programming

mcx - Express & compile probabilistic programs for performant inference on CPU & GPU

•    Python

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MCX is a probabilistic programming library with a laser-focus on sampling methods. MCX transforms the model definitions to generate logpdf or sampling functions. These functions are JIT-compiled with JAX; they support batching and can be exectuted on CPU, GPU or TPU transparently. The project is currently at its infancy and a moonshot towards providing sequential inference as a first-class citizen, and performant sampling methods for Bayesian deep learning.

probabilistic-programming

Turing.jl - The Turing language for probabilistic programming

•    Julia

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News: Turing.jl is now Julia 1.0 compatible now! Be aware that some things still might fail. Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include Hong Ge, Adam Scibior, Matej Balog, Zoubin Ghahramani, Kai Xu, Emma Smith, Emile Mathieu, Martin Trapp. You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.

machine-learning probabilistic-programming mcmc-sampler julia-language artificial-intelligence bayesian-inference

probreg - Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

•    Python

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Probreg is a library that implements point cloud registration algorithms with probablistic model. The point set registration algorithms using stochastic model are more robust than ICP(Iterative Closest Point). This package implements several algorithms using stochastic models and provides a simple interface with Open3D.

point-cloud registration gaussian-mixture-models expectation-maximization-algorithm variational-inference 3d dual-quaternion point-cloud-registration open3d coherent-point-drift non-rigid-registration rigid-transformations filterreg dual-quaternion-skinning

pystan - PyStan, the Python interface to Stan

•    Python

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PyStan provides a Python interface to Stan, a package for Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. For more information on Stan and its modeling language, see the Stan User's Guide and Reference Manual at http://mc-stan.org/.

stan statistics machine-learning probabilistic-programming

brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

•    R

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The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (i.e. models with multiple response variables) can be fitted, as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks, leave-one-out cross-validation, and Bayes factors. As a simple example, we use poisson regression to model the seizure counts in epileptic patients to investigate whether the treatment (represented by variable Trt) can reduce the seizure counts and whether the effect of the treatment varies with the baseline number of seizures a person had before treatment (variable log_Base4_c). As we have multiple observations per person, a group-level intercept is incorporated to account for the resulting dependency in the data.

brms stan bayesian-inference multilevel-models statistical-models r-package

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation

•    Python

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In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention. CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis. Alternatively, you can use the networkx drawing functionality for visualisations with fewer dependencies.

data-science machine-learning bayesian-inference bayesian-networks causal-inference causal-models causal-networks causalnex

JProGraM

•    Java

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JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (

pmtk3 - Probabilistic Modeling Toolkit for Matlab/Octave.

•    HTML

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PMTK is a collection of Matlab/Octave functions, written by Matt Dunham, Kevin Murphy and various other people. The toolkit is primarily designed to accompany Kevin Murphy's textbook Machine learning: a probabilistic perspective, but can also be used independently of this book. The goal is to provide a unified conceptual and software framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist statistics, such as cross validation, are also supported.) Since December 2011, the toolbox is in maintenance mode, meaning that bugs will be fixed, but no new features will be added (at least not by Kevin or Matt). PMTK builds on top of several existing packages, available from pmtksupport, and provides a unified interface to them. In addition, it provides readable "reference" implementations of many common machine learning techniques. The vast majority of the code is written in Matlab. (For a brief discussion of why we chose Matlab, click here. Most of the code also runs on Octave an open-source Matlab clone.) However, in a few cases we also provide wrappers to implementations written in C, for speed reasons. PMTK currently (October 2010) has over 67,000 lines.

bayadera - High-performance Bayesian Data Analysis on the GPU in Clojure

•    Clojure

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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-carlo

covid19model - Code for modelling estimated deaths and cases for COVID19.

•    Stan

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Code for modelling estimated deaths and infections for COVID-19 from "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe", Flaxman, Mishra, Gandy et al, Nature, 2020, the published version of our original Report 13. If you are looking for the individual based model used in Imperial's Report 9, Ferguson, Laydon, Nedjati-Gilani et al, please look here.

statistical-inference bayesian-statistics statistical-models probabilistic-models statistical-computing intervention-study branching-process renewal-process covid-19

bayes-nn - Lecture notes on Bayesian deep learning

•    

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Lecture notes on Bayesian deep learning

deep-learning probability-theory variational-inference

dowhy - DoWhy is a Python library that makes it easy to estimate causal effects

•    Python

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As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning. Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.

causal-inference python3 graphical-models