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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/.

http://mc-stan.org/interfaces/pystanhttps://github.com/stan-dev/pystan

Tags | stan statistics machine-learning probabilistic-programming |

Implementation | Python |

License | GPL |

Platform | Windows Linux |

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-programmingThere 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-analysisTensorFlow 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-programmingRStan is the R interface to Stan. RStan's source code repository is hosted here on GitHub. Stan's source repository is defined as a submodule. See how to work with stan submodule in rstan repo.

stan mcmc bayesian-inference bayesian-data-analysis bayesian-statistics r r-packageThe 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 statisticsPyro 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 uberPMTK 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.

The Stan Math Library is a C++, reverse-mode automatic differentiation library designed to be usable, extensive and extensible, efficient, scalable, stable, portable, and redistributable in order to facilitate the construction and utilization of algorithms that utilize derivatives. The Stan Math Library is licensed under the new BSD license.

stan stan-math-library math eigen boost automatic-differentiation cvodePyMC3 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 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-packageEvery week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something. While these started as opportunities to collectively "raise the tide" on common stumbling blocks in data munging and analysis tasks, they have since grown to machine learning, statistics, and general programming topics. Anything that will help us do our jobs better is fair game.

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1000+页)和视频链接

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

jupyter-notebook machine-learning book books probability probability-theory statistics statistics-course statistical-analysis statistical-learning statistical-testsNews: 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-inferenceThis repository holds open source Stan models, data simulators, and real data. There are models translating those found in books, most of the BUGS examples, and some basic examples used in the manual. The idea is to encourage people to go through all of these steps for their models, particularly 3 and 4, which often get overlooked. And if the example cannot be executed via rstan::stan_demo(), then something is wrong.

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegUpdate April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.

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-inferenceJMLR-MLOSS Manuscript Please consider citing it if you used it in your academic work. pomegranate is a package for probabilistic models in Python that is implemented in cython for speed. It's focus is on merging the easy-to-use scikit-learn API with the modularity that comes with probabilistic modeling to allow users to specify complicated models without needing to worry about implementation details. The models are built from the ground up with big data processing in mind and so natively support features like out-of-core learning and parallelism.

machine-learning probabilistic-graphical-models cython parallelA java decision supporting application for the urban planning domain. It is designed as a desktop frontend to a Mixed Integer Programming engine. Actually it uses the GNU Linear Programming Kit (GLPK).

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