BruntonUWBio / STIMDLinks
☆11Updated 7 years ago
Alternatives and similar repositories for STIMD
Users that are interested in STIMD are comparing it to the libraries listed below
Sorting:
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 5 years ago
- ☆15Updated 2 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆50Updated 2 years ago
- Implementation of the Gaussian Process Autoregressive Regression Model☆68Updated 10 months ago
- Gaussian processes regression models with linear inequality constraints☆15Updated last year
- Codes for Hilbert space reduced-rank GP regression☆14Updated 6 years ago
- Heterogeneous Multi-output Gaussian Processes☆54Updated 5 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆32Updated last year
- Continual Gaussian Processes☆31Updated 2 years ago
- ☆21Updated 7 years ago
- Stochastic variational heteroscedastic Gaussian process☆15Updated 6 years ago
- RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical sy…☆96Updated 2 years ago
- Embed strange attractors using a regularizer for autoencoders☆133Updated 4 years ago
- ☆30Updated 7 years ago
- Sequential Neural Likelihood☆42Updated 6 years ago
- State-space deep Gaussian processes in Python and Matlab☆30Updated 3 years ago
- Provides various extensions to the GPML toolbox for Gaussian process inference in MATLAB.☆34Updated 8 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 5 years ago
- Linear and non-linear spectral forecasting algorithms☆138Updated 4 years ago
- Variational Gaussian Process State-Space Models☆25Updated 10 years ago
- Data-driven modeling of chaotic systems in the form of SDEs and nonlinear observation maps☆19Updated 5 years ago
- Bayesian Dynamic Mode Decomposition (Bayesian DMD)☆18Updated 3 years ago
- Python and MATLAB code for Stein Variational sampling methods☆26Updated 6 years ago
- A non-parametric Bayesian approach to Hidden Markov Models☆87Updated 2 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 2 years ago
- Dynamic mode decomposition with dependent structure among observables (Graph DMD)☆14Updated 5 years ago
- Foundations and Applications☆101Updated 5 years ago