Add link to Arxiv article

fmorenopino / HeterogeneousHMM

Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types of probablistic models.

https://pyhhmm.readthedocs.io/en/latest/

Apache License 2.0

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Add link to Arxiv article #4

Closed impredicative closed 2 years ago

impredicative commented 2 years ago

Thanks for the package. The following new Arxiv article link can probably be added to the readme for reference purposes:

https://arxiv.org/abs/2201.06968

PyHHMM: A Python Library for Heterogeneous Hidden Markov Models

fmorenopino commented 2 years ago

Hi! References already added.