AaltoML / SDELinks
Example codes for the book Applied Stochastic Differential Equations
☆195Updated 4 years ago
Alternatives and similar repositories for SDE
Users that are interested in SDE are comparing it to the libraries listed below
Sorting:
- Manifold Markov chain Monte Carlo methods in Python☆232Updated last month
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆281Updated 3 years ago
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆185Updated 11 years ago
- 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.☆234Updated last year
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆42Updated 2 years ago
- A Python library for mathematical optimization☆141Updated 10 months ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆100Updated 2 years ago
- Python and MATLAB code for Stein Variational sampling methods☆25Updated 6 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated 9 months ago
- Tutorial materials of the Probabilistic Numerics Spring School.☆35Updated 2 years ago
- Heterogeneous Multi-output Gaussian Processes☆52Updated 5 years ago
- Nonparametric Differential Equation Modeling☆54Updated last year
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆456Updated 11 months ago
- ☆241Updated 2 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- Just a little MCMC☆227Updated last year
- Notes for the Numerics of Machine Learning Lecture Course at the University of Tübingen☆210Updated last year
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆405Updated last year
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆93Updated 3 years ago
- Proximal optimization in pure python☆118Updated 3 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Deep neural network kernel for Gaussian process☆208Updated 4 years ago
- Manifold-learning flows (ℳ-flows)☆230Updated 4 years ago
- Gaussian process modelling in Python☆223Updated 7 months ago