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

• URL: http://arxiv.org/abs/2605.29208v1
• Date: Thu, 28 May 2026 00:42:19 GMT
• Title: libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps
• Abstract summary: libhmm is a C++20 library for model parameter estimation, sequence decoding, and model selection.<n>It addresses two gaps in existing software: absence of a well-maintained, zero-dependency C++ HMM library suitable for embedding in production systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 library suitable for embedding in production systems, and the widespread use of method-of-moments (MOM) approximations in the emission distribution M-step of the Baum-Welch algorithm. The library implements correct maximum likelihood estimators for sixteen continuous and discrete emission distributions, including an ECME algorithm for the location-scale Student-t distribution, Newton-Raphson maximization for Gamma, Beta, Weibull, and Negative Binomial distributions, and the von Mises distribution for circular data. All forward-backward and Viterbi calculations operate in full log-space. SIMD acceleration is provided for AVX-512, AVX2, SSE2, and ARM NEON via compile-time dispatch with scalar fallback. Python bindings are available via the companion package pylibhmm. We compare libhmm against established C and C++ HMM libraries and against published R reference packages on five real-data benchmarks, and discuss the architectural tradeoffs made in the design.

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