Alternatives and similar repositories for MCMC

Users that are interested in MCMC are comparing it to the libraries listed below

• Joseph94m / MCMC
  Implementation of Markov Chain Monte Carlo in Python from scratch
  ☆213Updated 4 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆234Updated 4 years ago
• dilinwang820 / Stein-Variational-Gradient-Descent
  code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
  ☆404Updated last year
• dfm / gp
  A tutorial about Gaussian process regression
  ☆188Updated 4 years ago
• wiseodd / probabilistic-models
  Collection of probabilistic models and inference algorithms
  ☆241Updated 5 years ago
• josejimenezluna / pyGPGO
  Bayesian optimization for Python
  ☆246Updated 3 years ago
• PredictiveScienceLab / uq-course
  Introduction to Uncertainty Quantification
  ☆253Updated 3 years ago
• gdroguski / GaussianProcesses
  Python3 project applying Gaussian process regression for forecasting stock trends
  ☆153Updated 6 years ago
• brain-research / nngp
  Deep neural network kernel for Gaussian process
  ☆208Updated 4 years ago
• jamesrobertlloyd / gp-structure-search
  ☆236Updated 8 years ago
• automl / pybnn
  Bayesian neural network package
  ☆148Updated 4 years ago
• NicolasDurrande / Gaussian-Process-Regression-handout
  ☆39Updated 8 years ago
• wujian16 / Cornell-MOE
  A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
  ☆270Updated 5 years ago
• matt-graham / mici
  Manifold Markov chain Monte Carlo methods in Python
  ☆230Updated last month
• kamperh / bayes_gmm
  Bayesian Gaussian mixture models in Python.
  ☆63Updated 2 years ago
• mcdickenson / em-gaussian
  Python code for Expectation-Maximization estimate of Gaussian mixture model
  ☆75Updated 6 years ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆216Updated 6 years ago
• pmorenoz / HetMOGP
  Heterogeneous Multi-output Gaussian Processes
  ☆52Updated 5 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• Rachnog / Neural-Networks-for-Differential-Equations
  UNIVR PDE course project and just for fun
  ☆117Updated 8 years ago
• ssydasheng / Bayesian_neural_network_papers
  Papers for Bayesian-NN
  ☆325Updated 6 years ago
• GPflow / GPflowOpt
  Bayesian Optimization using GPflow
  ☆272Updated 4 years ago
• duvenaud / phd-thesis
  I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth
  ☆185Updated 11 years ago
• thuijskens / bayesian-optimization
  Python code for bayesian optimization using Gaussian processes
  ☆311Updated 8 years ago
• mfouesneau / NUTS
  python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011
  ☆132Updated 4 years ago
• leowyy / mcmc-importance-sampling
  Markov Chain Monte Carlo (MCMC) and importance sampling in the context of Bayesian linear regression
  ☆11Updated 7 years ago
• Gabriel-p / pythonMCMC
  A list of Python-based MCMC & ABC packages
  ☆123Updated last month
• axelbrando / Mixture-Density-Networks-for-distribution-and-uncertainty-estimation
  A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
  ☆349Updated 8 years ago
• fonnesbeck / gp_regression
  A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)
  ☆165Updated 3 years ago
• kumar-shridhar / Master-Thesis-BayesianCNN
  Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
  ☆264Updated 6 years ago