Alternatives and similar repositories for gp_grief

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

• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• karlnapf / kernel_exp_family
  Various estimators of the infinite dimensional exponential family model
  ☆16Updated 8 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
• duvenaud / jaxde
  Prototypes of differentiable differential equation solvers in JAX.
  ☆27Updated 5 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆65Updated 6 years ago
• ebonilla / AutoGP
  Code for AutoGP
  ☆27Updated 6 years ago
• jmetzen / bayesian_optimization
  Bayesian optimization
  ☆38Updated 6 years ago
• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 8 years ago
• AaltoML / boundary-gp
  Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
  ☆24Updated 6 years ago
• oxfordcontrol / Bayesian-Optimization
  Reference implementation of Optimistic Expected Improvement.
  ☆50Updated 5 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• karlnapf / kernel_hmc
  Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"
  ☆26Updated 7 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• cvxgrp / ncvx
  ☆77Updated 3 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 7 years ago
• karlnapf / kameleon-mcmc
  Code for Kernel Adaptive Metropolis-Hastings
  ☆33Updated 10 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 5 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆64Updated 6 years ago
• izmailovpavel / TTGP
  ☆25Updated 7 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆39Updated 6 years ago
• sods / paramz
  Parameterization Framework for parameterized model creation and handling.
  ☆49Updated 5 months ago
• aboustati / dgplib
  Library for Deep Gaussian Processes based on GPflow
  ☆18Updated 5 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆110Updated 7 years ago
• matt-graham / mici
  Manifold Markov chain Monte Carlo methods in Python
  ☆236Updated last week
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆116Updated 12 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆85Updated 4 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 8 years ago
• osqp / osqpth
  The differentiable OSQP solver layer for PyTorch
  ☆64Updated 2 years ago
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆102Updated 2 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆112Updated 4 years ago