Alternatives and similar repositories for Bayesian-Filtering

Users that are interested in Bayesian-Filtering are comparing it to the libraries listed below

• cvxgrp / auto_ks
  Repository for "Fitting a Kalman Smoother to Data"
  ☆61Updated last year
• EEA-sensors / Bayesian-Filtering-and-Smoothing
  Companion Matlab and Python codes for the book Bayesian Filtering and Smoothing by Simo Särkkä and Lennart Svensson
  ☆82Updated last year
• AaltoML / IHGP
  Infinite-horizon Gaussian processes
  ☆33Updated 5 years ago
• sorooshafiee / WKF
  ☆16Updated 7 years ago
• tingiskhan / pyfilter
  Particle filtering and sequential parameter inference in Python
  ☆83Updated 2 years ago
• erichanslee / GP_Derivatives
  Scalable Gaussian Process Regression with Derivatives
  ☆38Updated 7 years ago
• EEA-sensors / parallel-non-linear-gaussian-smoothers
  Companion code in JAX for the paper Parallel Iterated Extended and Sigma-Point Kalman Smoothers.
  ☆27Updated last year
• jnez71 / kalmaNN
  Extended Kalman filter for training neural-networks
  ☆96Updated 5 years ago
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆102Updated 2 years ago
• wjmaddox / online_gp
  Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"
  ☆64Updated 4 years ago
• jacobnzw / SSMToybox
  Nonlinear Sigma-Point Kalman Filters based on Bayesian Quadrature
  ☆12Updated 4 years ago
• thangbui / streaming_sparse_gp
  Streaming sparse Gaussian process approximations
  ☆69Updated 3 years ago
• manschuer / SRGP
  stochastic recursive Gaussian Process (SRGP)
  ☆20Updated 5 years ago
• VBayesLab / VBLab
  Official documentation
  ☆31Updated 4 years ago
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆101Updated 3 months ago
• linesd / SSGPR
  Sparse Spectrum Gaussian Process Regression
  ☆25Updated 5 years ago
• AaltoML / BayesNewton
  Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
  ☆241Updated 2 years ago
• wjmaddox / online_vargp
  Online variational GPs
  ☆37Updated 2 years ago
• EEA-sensors / sequential-parallelization-examples
  This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.
  ☆33Updated 2 years ago
• alshedivat / gpml
  An up-to-date version of GPML library.
  ☆37Updated 6 years ago
• boisgera / control-engineering-with-python
  Control Engineering with Python
  ☆77Updated 3 months ago
• st-- / interactive-gp-visualization
  Interactive visualization of Gaussian processes
  ☆176Updated 2 years ago
• OxfordML / GPz
  GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data
  ☆48Updated 4 years ago
• jerkern / pyParticleEst
  Python Library for Particle based estimation methods
  ☆64Updated 7 years ago
• AlexanderFabisch / gmr
  Gaussian Mixture Regression
  ☆197Updated 4 months ago
• zgbkdlm / ssdgp
  State-space deep Gaussian processes in Python and Matlab
  ☆30Updated 3 years ago
• MarcoTodescato / Efficient-GP-Regression-via-Kalman-Filtering
  Code to implement efficient spatio-temporal Gaussian Process regression via iterative Kalman Filtering. KF is used to resolve the tempora…
  ☆39Updated 7 years ago
• sailinglab / pgm-spring-2019
  Course webpage for PGM, Spring 2019.
  ☆76Updated 4 years ago
• StanfordASL / SNDL
  Stabilizable Nonlinear Dynamics Learning
  ☆22Updated 6 years ago
• tu-rbo / differentiable-particle-filters
  Source code and data for the paper "Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors"
  ☆189Updated 2 years ago