New C++ library libhmm offers accurate HMM parameter estimation

A new C++20 library called libhmm has been developed for Hidden Markov Models (HMMs), addressing a lack of well-maintained, embeddable C++ HMM software. It corrects the common use of approximations in the Baum-Welch algorithm's emission distribution M-step by implementing accurate maximum likelihood estimators for various continuous and discrete distributions. The library also features log-space calculations for forward-backward and Viterbi algorithms, SIMD acceleration, and Python bindings, with performance validated against existing libraries and R packages. AI

IMPACT Provides a more accurate and efficient tool for researchers and developers working with Hidden Markov Models.

RANK_REASON The item describes a new software library for a specific machine learning technique, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• Baum-Welch algorithm
• C++20
• Hidden Markov Model
• libhmm
• Python
• R

• paper
• infra

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