Alternatives and similar repositories for py-mcmc

Users that are interested in py-mcmc are comparing it to the libraries listed below

• dfm / george
  Fast and flexible Gaussian Process regression in Python
  ☆459Updated last week
• OxfordML / GPz
  GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data
  ☆49Updated 4 years ago
• karlnapf / kernel_hmc
  Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"
  ☆26Updated 7 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
  ☆188Updated 11 years ago
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆84Updated last year
• sods / paramz
  Parameterization Framework for parameterized model creation and handling.
  ☆49Updated 6 months ago
• sanjibs / bmcmc
  MCMC package for Bayesian data analysis
  ☆59Updated 8 years ago
• dfm / gp
  A tutorial about Gaussian process regression
  ☆192Updated 5 years ago
• mfouesneau / NUTS
  python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011
  ☆131Updated 5 years ago
• thangbui / geepee
  A collection of Gaussian process models
  ☆30Updated 8 years ago
• Sohl-Dickstein / LAHMC
  Look Ahead Hamiltonian Monte Carlo
  ☆30Updated 10 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆93Updated 4 years ago
• dfm / celerite
  Scalable 1D Gaussian Processes in C++, Python, and Julia
  ☆191Updated last week
• pcubillos / mc3
  Python MCMC Sampler
  ☆33Updated 10 months ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆217Updated 6 years ago
• kbarbary / nestle
  Pure Python, MIT-licensed implementation of nested sampling algorithms for evaluating Bayesian evidence.
  ☆74Updated 3 months ago
• svaiter / pyprox
  Proximal algorithms made easy in Python
  ☆59Updated 8 years ago
• EliseJ / astroABC
  Approximate Bayesian Computation Sequential Monte Carlo sampler for parameter estimation.
  ☆39Updated 5 years ago
• markchil / gptools
  Gaussian processes with arbitrary derivative constraints and predictions.
  ☆45Updated 6 years ago
• exoml / plan-net
  An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
  ☆12Updated 6 years ago
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆116Updated 12 years ago
• epfl-lts2 / pyunlocbox
  A Python convex optimization package using proximal splitting methods
  ☆119Updated 4 months ago
• brain-research / l2hmc
  TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
  ☆183Updated 7 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆236Updated 4 years ago
• maziarraissi / ParametricGP
  Parametric Gaussian Process Regression for Big Data
  ☆45Updated 5 years ago
• pmelchior / pygmmis
  Gaussian mixture model for incomplete (missing or truncated) and noisy data
  ☆103Updated 3 years ago
• dflemin3 / approxposterior
  A Python package for approximate Bayesian inference and optimization using Gaussian processes
  ☆43Updated 2 years ago
• gpapamak / epsilon_free_inference
  Code for paper "Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation"
  ☆32Updated 6 years ago
• mebden / GPtutorial
  Matlab code for the introduction to Gaussian processes, 2008
  ☆30Updated 10 years ago
• elfi-dev / elfi
  ELFI - Engine for Likelihood-Free Inference
  ☆279Updated 8 months ago