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New C++ library libhmm offers accurate HMM parameter estimation

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New C++ library libhmm offers accurate HMM parameter estimation

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gary Wolfman ·

    libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps

    arXiv:2605.29208v1 Announce Type: cross Abstract: We describe libhmm, a C++20 library for Hidden Markov Model parameter estimation, sequence decoding, and model selection. libhmm addresses two gaps in existing software: the absence of a well-maintained, zero-dependency C++ HMM li…

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