Alternatives and similar repositories for Gaussian-Process-Regression-Tutorial:

Users that are interested in Gaussian-Process-Regression-Tutorial are comparing it to the libraries listed below

• jwangjie / gpytorch
  A highly efficient and modular implementation of Gaussian Processes in PyTorch
  ☆16Updated 4 years ago
• GAMES-UChile / mogptk
  Multi-Output Gaussian Process Toolkit
  ☆166Updated 9 months ago
• IntelLabs / bayesian-torch
  A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
  ☆561Updated last week
• Harry24k / bayesian-neural-network-pytorch
  PyTorch implementation of bayesian neural network [torchbnn]
  ☆506Updated 5 months ago
• nchopin / particles
  Sequential Monte Carlo in python
  ☆428Updated 3 months ago
• mksadoughi / Multi-output-Gaussian-Process
  ☆14Updated 6 years ago
• Magica-Chen / gptp_multi_output
  multivariate Gaussian process regression and multivariate Student-t process regression
  ☆70Updated 3 years ago
• dfm / gp
  A tutorial about Gaussian process regression
  ☆185Updated 4 years ago
• french-paragon / BayesianNeuralNetwork-Tutorial-Metarepos
  A meta repository pointing to the other repositories where the implementation of the supplementary examples for our tutorial "Hands-on Ba…
  ☆119Updated 2 years ago
• coursekevin / svise
  Accompanying code for "State Estimation of a Physical System without Governing Equations"
  ☆82Updated 6 months ago
• gdroguski / GaussianProcesses
  Python3 project applying Gaussian process regression for forecasting stock trends
  ☆152Updated 6 years ago
• dynamicslab / pykoopman
  A package for computing data-driven approximations to the Koopman operator.
  ☆325Updated 2 months ago
• jelfring / particle-filter-tutorial
  ☆120Updated 2 months ago
• borgwang / toys
  Toy models, experiments and random notes on machine learning and deep learning.
  ☆125Updated 3 months ago
• TyXe-BDL / TyXe
  ☆147Updated 2 years ago
• BethanyL / DeepKoopman
  neural networks to learn Koopman eigenfunctions
  ☆387Updated 9 months ago
• sydney-machine-learning / Bayesianneuralnetworks-MCMC-tutorial
  Bayesian neural networks via MCMC: tutorial
  ☆43Updated 2 months ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆232Updated 3 years ago
• AdamCobb / hamiltorch
  PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
  ☆429Updated 4 months ago
• OxfordML / GPz
  GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data
  ☆48Updated 3 years ago
• maziarraissi / TutorialGP
  Tutorial on Gaussian Processes
  ☆62Updated 4 years ago
• drewwilimitis / Manifold-Learning
  Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Emb…
  ☆229Updated 4 years ago
• Ulti-Dreisteine / gaussian-process-regression
  高斯过程回归
  ☆75Updated 2 years ago
• KrisNguyen135 / bayesian-optimization-in-action
  Source code for Bayesian Optimization in Action, published by Manning
  ☆83Updated last year
• SheffieldML / GPy
  Gaussian processes framework in python
  ☆2,064Updated this week
• sbi-dev / sbi
  Simulation-based inference toolkit
  ☆616Updated this week
• MilesCranmer / lagrangian_nns
  Lagrangian Neural Networks
  ☆473Updated 6 months ago
• AlexanderFabisch / gmr
  Gaussian Mixture Regression
  ☆177Updated last month
• JaxGaussianProcesses / GPJax
  Gaussian processes in JAX.
  ☆473Updated last month
• samuelkim314 / DeepSymReg
  Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
  ☆70Updated last year