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
  ☆232Updated 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
  ☆183Updated 10 years ago
• elfi-dev / elfi
  ELFI - Engine for Likelihood-Free Inference
  ☆272Updated 8 months ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆134Updated 4 years ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆166Updated 2 years ago
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated 8 months ago
• jameshensman / VFF
  Variational Fourier Features
  ☆84Updated 3 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated 8 months ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆149Updated 6 years ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆148Updated 8 months ago
• slinderman / pypolyagamma
  Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
  ☆82Updated 4 years ago
• vlad17 / runlmc
  Structurally efficient multi-output linearly coregionalized Gaussian Processes: it's tricky, tricky, tricky, tricky, tricky.
  ☆37Updated 2 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆92Updated 3 years ago
• dfm / gp
  A tutorial about Gaussian process regression
  ☆186Updated 4 years ago
• skggm / skggm
  Scikit-learn compatible estimation of general graphical models
  ☆247Updated last year
• 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
• GPflow / GPflowOpt
  Bayesian Optimization using GPflow
  ☆272Updated 4 years ago
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆214Updated 6 years ago
• PredictiveScienceLab / pysmc
  Sequential Monte Carlo working on top of pymc
  ☆50Updated 6 years ago
• rmgarnett / gpml_extensions
  Provides various extensions to the GPML toolbox for Gaussian process inference in MATLAB.
  ☆31Updated 8 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆233Updated 3 years ago
• dfm / george
  Fast and flexible Gaussian Process regression in Python
  ☆456Updated this week
• gradientinstitute / aboleth
  A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
  ☆128Updated 3 years ago
• markvdw / GParML
  Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
  ☆43Updated 11 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆105Updated 7 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 years ago
• mattjj / pyslds
  ☆94Updated 6 years ago
• mfouesneau / NUTS
  python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011
  ☆128Updated 4 years ago
• twiecki / WhileMyMCMCGentlySamples
  my blog
  ☆266Updated 2 years ago