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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.

Probability is the mathematics of reasoning under uncertainty, much as calculus is the mathematics for reasoning about rates of change. Models built in the language of probability can capture complex reasoning, know what they do not know, and uncover structure in data without supervision. Further, probability provides a way for human experts to provide knowledge to AI systems in the form of a priori beliefs.

Specifying probabilistic models directly can be cumbersome and implementing them can be very error-prone. Probabilistic programming languages (PPLs) solve these problems by marrying probability with the representational power of programming languages. A probabilistic program is a mix of ordinary deterministic computation and randomly sampled values; this stochastic computation represents a generative story about data.

https://github.com/uber/pyro

Tags | pytorch machine-learning bayesian webppl inference probabilistic-programming probabilistic-graphical-models bayesian-inference variational-inference uber |

Implementation | Python |

License | Public |

Platform | Windows Linux |

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-modelingPyMC3 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 bayesianThe 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 statisticsInfer.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-inferenceEdward 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-programmingOrbit 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-smoothingProbabilistic 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-algorithmsTensorFlow 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-programmingMCX 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-programmingNews: 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-inferenceProbreg 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-skinningPyStan 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-programmingThe 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-packageIn 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 causalnexJProGraM (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 (

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.

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-carloCode 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-19Lecture notes on Bayesian deep learning

deep-learning probability-theory variational-inferenceAs 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
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