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CHMMPP: A c++ library for constrained Hidden Markov Models

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/chmmpp
https://doi.org/10.11578/dc.20241009.3

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Abstract

SAND2024-13027O The CHMMPP: A c++ Library for Constrained Hidden Markov Models (HMM) software supports the analysis of multivariate time series data to detect patterns using HMM. Many applications involve the detection and characterization of hidden or latent states in a complex system using observable states and variables. This software supports inference of latent states integrating both an HMM and application-specific constraints that reflect known relationships in hidden states. The CHMMPP software supports application-specific and generic methods for constrained inference. This includes a framework for customized Viterbi methods, constrained inference of hidden states with A* and integer programming methods, and various constraint-informed methods for learning HMM model parameters. CHMMPP focuses on supporting generic methods that enable the agile expression of complex sets of constraints that naturally arise in many real-world applications.
Developers:
Hart, William [1][2][3] ;  Mattes, Connor [1][2][3]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
Release Date:
2024-05-23
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
C++
Version:
1.0.0
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
145407
Site Accession Number:
SCR #3025.0
Research Org.:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/chmmpp
https://doi.org/10.11578/dc.20241009.3

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Citation Formats

Hart, William, and Mattes, Connor. CHMMPP: A c++ library for constrained Hidden Markov Models. Computer Software. https://github.com/sandialabs/chmmpp. USDOE. 23 May. 2024. Web. doi:10.11578/dc.20241009.3.
Hart, William, & Mattes, Connor. (2024, May 23). CHMMPP: A c++ library for constrained Hidden Markov Models. [Computer software]. https://github.com/sandialabs/chmmpp. https://doi.org/10.11578/dc.20241009.3.
Hart, William, and Mattes, Connor. "CHMMPP: A c++ library for constrained Hidden Markov Models." Computer software. May 23, 2024. https://github.com/sandialabs/chmmpp. https://doi.org/10.11578/dc.20241009.3.
@misc{ doecode_145407,
title = {CHMMPP: A c++ library for constrained Hidden Markov Models},
author = {Hart, William and Mattes, Connor},
abstractNote = {SAND2024-13027O The CHMMPP: A c++ Library for Constrained Hidden Markov Models (HMM) software supports the analysis of multivariate time series data to detect patterns using HMM. Many applications involve the detection and characterization of hidden or latent states in a complex system using observable states and variables. This software supports inference of latent states integrating both an HMM and application-specific constraints that reflect known relationships in hidden states. The CHMMPP software supports application-specific and generic methods for constrained inference. This includes a framework for customized Viterbi methods, constrained inference of hidden states with A* and integer programming methods, and various constraint-informed methods for learning HMM model parameters. CHMMPP focuses on supporting generic methods that enable the agile expression of complex sets of constraints that naturally arise in many real-world applications.},
doi = {10.11578/dc.20241009.3},
url = {https://doi.org/10.11578/dc.20241009.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20241009.3}},
year = {2024},
month = {may}
}