pyhsmm
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
What is pyhsmm?
pyhsmm is a Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs). It enables unsupervised learning of time-series data by inferring hidden state sequences, transition dynamics, and model parameters using Bayesian nonparametric methods like the Hierarchical Dirichlet Process (HDP).
Researchers and data scientists working on time-series analysis, particularly those interested in Bayesian nonparametric methods, unsupervised learning, and flexible model selection for sequential data.
It provides a specialized implementation of HDP-HMM and HDP-HSMM with weak-limit approximations, offering automatic state count inference and extensible distributions, which is less common in general-purpose probabilistic programming libraries.
Overview
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.
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