skpro

pyro

Deep universal probabilistic programming with Python and PyTorch

GPyTorch

A highly efficient implementation of Gaussian Processes in PyTorch

pyhsmm

pyhsmm is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs). It focuses on Bayesian Nonparametric extensions like the HDP-HMM and HDP-HSMM, primarily using weak-limit approximations for scalable inference. ## Key Features - **Bayesian Nonparametric Models** — Implements Hierarchical Dirichlet Process (HDP) priors for HMMs and HSMMs to infer the number of states automatically. - **Weak-Limit Approximations** — Uses computationally efficient approximations for inference in nonparametric models. - **Gibbs Sampling** — Performs approximate posterior inference via Gibbs sampling over latent state sequences, transition matrices, and parameters. - **Extensible Distributions** — Supports custom observation and duration distributions by implementing defined interfaces. - **Multiple Data Sequences** — Allows learning from multiple observation sequences by adding each to the model. ## Philosophy pyhsmm emphasizes Bayesian nonparametric approaches to model selection and uncertainty quantification, providing tools for flexible time-series modeling without pre-specifying the number of hidden states.

sklearn-bayes

Python package for Bayesian Machine Learning with scikit-learn API