nbfigueroa / ICSC-HMMLinks
Toolbox for IBP Coupled SPCM-CRP Hidden Markov Model. Also contains code for EM-based HMM learning and inference for Bayesian non-parametric HDP-HMM and IBP-HMM.
☆14Updated 6 years ago
Alternatives and similar repositories for ICSC-HMM
Users that are interested in ICSC-HMM are comparing it to the libraries listed below
Sorting:
- Python implementation of the PR-SSM.☆56Updated 7 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 3 years ago
- Continual Gaussian Processes☆31Updated 2 years ago
- ☆47Updated 3 years ago
- ☆21Updated 6 years ago
- ☆16Updated 7 years ago
- Python library for Recurrent Gaussian Processes☆22Updated 8 years ago
- Implementation of the Gaussian processes regression with inducing points for online data with ensemble Kalman filter estimation. Code for…☆17Updated 7 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆24Updated 6 years ago
- Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) implemented purely on numpy☆72Updated 6 years ago
- ☆26Updated 5 years ago
- Nonparametric Bayesian Inference for Sequential Data. Includes state-of-the-art MCMC inference for Beta process Hidden Markov Models (BP…☆78Updated 7 years ago
- Applications of Gaussian Process Latent Variable Models in Finance☆11Updated 3 years ago
- Gaussian Mixture Regression☆197Updated 4 months ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 7 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Variational Auto-Regressive Gaussian Processes for Continual Learning☆22Updated 4 years ago
- This is a re-implementation and test on paper Deep Kalman Filter: https://arxiv.org/pdf/1511.05121.pdf☆19Updated 6 years ago
- Infinite-horizon Gaussian processes☆33Updated 5 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Code for "Meta-Learning Priors for Efficient Online Bayesian Regression" by James Harrison, Apoorva Sharma, and Marco Pavone☆54Updated 2 years ago
- Great resources for learning optimal control☆18Updated 6 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"☆39Updated last year
- Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)☆33Updated 6 years ago
- Particle Filter Recurrent Neural Networks (AAAI 2020)☆78Updated last year
- A non-parametric Bayesian approach to Hidden Markov Models☆86Updated 2 years ago
- Maximum Causal Entropy Inverse Reinforcement Learning☆48Updated 7 years ago
- Variational Inference in Gaussian Mixture Model☆61Updated 5 years ago
- Autoregressive policies for continuous control reinforcement learning☆32Updated 6 years ago