Hidden Markov Models
Showing 8 of 8 projects
A comprehensive collection of machine learning algorithms implemented exclusively in NumPy for educational purposes and prototyping.
A Python library for probabilistic modeling built on PyTorch, offering modular distributions, GPU support, and flexible model composition.
A Python library for probabilistic state space modeling and inference, built on JAX.
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
A Python package providing Bayesian machine learning algorithms with a scikit-learn compatible API.
TensorFlow and NumPy implementations of HMM Viterbi and forward/backward algorithms for sequence modeling.
A Python probabilistic programming framework for objective model selection in time-varying parameter time series models.
A Clojure/ClojureScript library for building self-contained natural language parsers using part-of-speech tagging and semantic rules.
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