Alternatives and similar repositories for gpss-research

Users that are interested in gpss-research are comparing it to the libraries listed below

• jamesrobertlloyd / gp-structure-search
  ☆237Updated 8 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
• elfi-dev / elfi
  ELFI - Engine for Likelihood-Free Inference
  ☆277Updated 2 months ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆168Updated 2 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆136Updated 4 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
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• PredictiveScienceLab / pysmc
  Sequential Monte Carlo working on top of pymc
  ☆50Updated 6 years ago
• GPflow / GPflowOpt
  Bayesian Optimization using GPflow
  ☆271Updated 4 years ago
• mcleonard / sampyl
  MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice
  ☆339Updated 2 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated last year
• jameshensman / VFF
  Variational Fourier Features
  ☆86Updated 4 years ago
• slinderman / pypolyagamma
  Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
  ☆82Updated 5 years ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆148Updated last year
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• HIPS / Probabilistic-Backpropagation
  Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
  ☆192Updated 6 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆106Updated 7 years ago
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆114Updated 12 years ago
• dfm / gp
  A tutorial about Gaussian process regression
  ☆188Updated 4 years ago
• skggm / skggm
  Scikit-learn compatible estimation of general graphical models
  ☆246Updated last month
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• ColCarroll / minimc
  Just a little MCMC
  ☆227Updated last year
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆234Updated 4 years ago
• sods / paramz
  Parameterization Framework for parameterized model creation and handling.
  ☆49Updated last month
• 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
• bnpy / bnpy
  Bayesian nonparametric machine learning for Python
  ☆225Updated 2 years ago
• vlad17 / runlmc
  Structurally efficient multi-output linearly coregionalized Gaussian Processes: it's tricky, tricky, tricky, tricky, tricky.
  ☆38Updated 2 years ago
• mmp2 / megaman
  megaman: Manifold Learning for Millions of Points
  ☆328Updated 2 years ago
• dfm / george
  Fast and flexible Gaussian Process regression in Python
  ☆460Updated last week
• twiecki / WhileMyMCMCGentlySamples
  my blog
  ☆268Updated 3 years ago