qu-gg / torch-neural-ssm
Neural State-Space Models and Latent Dynamics Functions in PyTorch for High-Dimensional Forecasting
☆31Updated 5 months ago
Related projects: ⓘ
- Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.☆88Updated 2 years ago
- ☆12Updated last year
- Discovers high dimensional models from 1D data using deep delay autoencoders☆27Updated last year
- A Brief Introduction to Path Signatures for Machine Learning Practitioners☆40Updated 3 years ago
- ☆40Updated last month
- ☆20Updated 2 months ago
- ☆11Updated 2 years ago
- Consistent Koopman Autoencoders☆63Updated last year
- A Python package to learn the Koopman operator.☆42Updated this week
- ☆72Updated last year
- Code for Hidden Markov Nonlinear ICA☆23Updated 2 years ago
- Particle filtering and sequential parameter inference in Python☆74Updated last year
- This repository contains research code for the paper "Generating realistic neurophysiological time series with denoising diffusion probab…☆52Updated 3 weeks ago
- Forecasting with PyTorch☆53Updated 2 weeks ago
- Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Su…☆75Updated 10 months ago
- Copula-GP model☆13Updated 10 months ago
- PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python☆113Updated 2 weeks ago
- Taylor moment expansion in Python (JaX and SymPy) and Matlab☆13Updated last year
- Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend☆58Updated 3 months ago
- ☆27Updated last year
- A non-parametric Bayesian approach to Hidden Markov Models☆83Updated last year
- Accompanying code for "State Estimation of a Physical System without Governing Equations"☆73Updated 2 months ago
- State-space deep Gaussian processes in Python and Matlab☆29Updated 2 years ago
- ☆12Updated 6 years ago
- A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.☆39Updated last month
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆28Updated 2 months ago
- A toolbox for inference of mixture models☆16Updated last year
- Recurrent Switching Linear Dynamical Systems☆94Updated last year
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated last year
- Implementation of the Gaussian Process Autoregressive Regression Model☆59Updated 3 years ago