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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 bayesianemcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature. Read the docs at emcee.readthedocs.io.

mcmc mcmc-sampler probabilistic-data-analysisto 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 stanRStan 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-packageOwl is an emerging numerical library for scientific computing and engineering. The library is developed in the OCaml language and inherits all its powerful features such as static type checking, powerful module system, and superior runtime efficiency. Owl allows you to write succinct type-safe numerical applications in functional language without sacrificing performance, significantly reduces the cost from prototype to production use. Owl's documentation contains a lot of learning materials to help you start. The full documentation consists of two parts: Tutorial Book and API Reference. Both are perfectly synchronised with the code in the repository by the automatic building system. You can access both parts with the following link.

matrix linear-algebra ndarray statistical-functions topic-modeling regression maths gsl plotting sparse-linear-systems scientific-computing numerical-calculations statistics mcmc optimization autograd algorithmic-differentation automatic-differentiation machine-learning neural-networkConvChain passes a sample image through a 1-layer lattice of small overlapping receptive fields. It then runs an MCMC simulation with obtained weights as coefficients in the energy functional. In the language of cellular automata, ConvChain takes an input image and builds a probabilistic cellular automaton that is most likely to generate that image.

procedural-generation machine-learning gamedev c-sharp texture-synthesis mcmc algorithm convchainA 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-carloThis package provides a Julia infrastructure for Simulated Method of Moments estimation, or other problems where we want to optimize a non-differentiable objective function. The setup is suitable for all kinds of likelihood-free estimators - in general, those require evaluating the objective at many regions. The user can supply their own algorithms for generating successive new parameter guesses. We provide a set of MCMC template algorithms. The code can be run in serial or on a cluster. Baragatti, Grimaud and Pommeret (BGP) in "Likelihood-free parallel tempring" propose an approximate Bayesian Computation (ABC) algorithm that incorporates the parallel tempering idea of Geyer (1991). We provide the BGP algorithm as a template called MAlgoBGP. Here we use it to run a simple toy example where we want to estimate the means of a bivariate normal distribution by using MCMC. We use 3 parallel chains, each with different temperature. The chains can exchange locations along the process if this is suitable.

julia mcmc estimationThis package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to derive posterior distributions of subsurface and measured data properties. The current implementation is applied to time and frequency domain electro-magnetic data. Application outside of these data types is well within scope. Currently there are two types of data that we have implemented; frequency domain electromagnetic data, and time domain electromagnetic data. The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller. See the section Installing the time domain forward modeller for more information.

bayesian-inference bayesian geophysics mcmc trans-dimensional parallel mpi4py h5py hdf5 mpiA Python implementation of the Markov-chain Monte Carlo algorithm. Find the full MC3 documentation at http://pcubillos.github.io/MCcubed.

mcmcCollection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.

machine-learning mcmc markov-chain-monte-carlo sampling-methods bayesian-inferenceCollection of examples of various probabilistic models and inference algorithms.

probabilistic-models machine-learning bayesian bayesian-inference mcmc gibbs-sampling variational-inference dirichlet-processThis repository contains some Matlab/Octave demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3). Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10 and 11. Furthermore there is a demo for MatlabStan.

bayesian-data-analysis bayesian bayesian-inference mcmc stan octaveThis repository contains some R demos and additional notes for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3). Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10 and 11. Furthermore there are demos for RStan and RStanARM. There are also reading instructions and additional notes for chapters 1-8, 10-11.

bayesian-data-analysis r bayesian-inference bayesian mcmc stan monte-carloShinyStan provides immediate, informative, customizable visual and numerical summaries of model parameters and convergence diagnostics for MCMC simulations. The ShinyStan interface is coded primarily in R using the Shiny web application framework and is available via the shinystan R package. Applied Bayesian data analysis is primarily implemented through the MCMC algorithms offered by various software packages. When analyzing a posterior sample obtained by one of these algorithms the first step is to check for signs that the chains have converged to the target distribution and and also for signs that the algorithm might require tuning or might be ill-suited for the given model. There may also be theoretical problems or practical inefficiencies with the specification of the model.

stan mcmc r-package statistical-graphics shiny-apps r bayesian bayesian-statistics bayesian-inference bayesian-data-analysis bayesian-methodsThis is a document that introduces Bayesian data analysis. It serves as a practical and applied introduction to Bayesian approaches for the uninitiated. The goal is to provide just enough information in a brief format to allow one to feel comfortable exploring Bayesian data analysis for themselves, assuming they have the requisite context to begin with. There is a shiny app to play with also.

bayesian-inference bayesian-methods stan rstan r bayesian-statistics bayesian mcmc hamiltonian-monte-carlo gibbs posterior-probability prior likelihood metropolis-hastings posterior-predictive regression-models bayesian-analysis bayesian-data-analysisbayesplot is an R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). Currently bayesplot offers a variety of plots of posterior draws, visual MCMC diagnostics, as well as graphical posterior predictive checking. Additional functionality (e.g. for forecasting/out-of-sample prediction and other inference-related tasks) will be added in future releases. The plots created by bayesplot are ggplot objects, which means that after a plot is created it can be further customized using the various functions for modifying ggplot objects provided by the ggplot2 package.

r-package statistical-graphics stan bayesian ggplot2 visualization mcmcNote that the code is in Jupyter Notebook format and requires modification to use with other datasets. Some of the general concepts from the book are discussed in papers by Kruschke & Liddell. See references below.

bayesian-data-analysis bayesian-inference pymc3 mcmc hierarchical-models kruschke probabilistic-programmingA very basic LDA (Latent Dirichlet Allocation) implementation with some convenient utilities.

lda topic model topic-model topics bayesian inference mcmc gibbsNew developments will be done in extra branches and will be tested before merging in the developtment branch, so the developmet version should usually be usable (consider it in a beta stage), while feature branches should be considered alpha. Windows users: the package contains c++ code, so if you compile yourself, you need RTools installed.

cran mcmc smc bayes ecological-models systems-biology optimization
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