AaltoML / SDELinks
Example codes for the book Applied Stochastic Differential Equations
☆202Updated 2 months ago
Alternatives and similar repositories for SDE
Users that are interested in SDE are comparing it to the libraries listed below
Sorting:
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆171Updated 3 years ago
- 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
- Heterogeneous Multi-output Gaussian Processes☆54Updated 5 years ago
- Manifold Markov chain Monte Carlo methods in Python☆237Updated 2 weeks ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆42Updated 2 years ago
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆287Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆112Updated 4 years ago
- ☆251Updated 3 years ago
- A Python library for mathematical optimization☆141Updated last year
- Python and MATLAB code for Stein Variational sampling methods☆26Updated 6 years ago
- Deep neural network kernel for Gaussian process☆212Updated 5 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated last year
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆95Updated 4 years ago
- Nonparametric Differential Equation Modeling☆56Updated last year
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆188Updated 11 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆74Updated 5 years ago
- Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support☆223Updated 5 years ago
- Gaussian process modelling in Python☆226Updated last year
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆462Updated last year
- Manifold-learning flows (ℳ-flows)☆231Updated 5 years ago
- Code for Neural Spline Flows paper☆283Updated 2 months ago
- Notes for the Numerics of Machine Learning Lecture Course at the University of Tübingen☆224Updated last year
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆102Updated 2 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆45Updated 7 years ago
- Sequential Neural Likelihood☆42Updated 6 years ago
- Deep Gaussian Processes in Python☆236Updated 4 years ago
- This repository contains code released by DiffEqML Research☆92Updated 3 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆410Updated last year
- Continual Gaussian Processes☆31Updated 2 years ago