Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models

Authors

Alexandre Mösching (ORCID: https://orcid.org/0000-0002-8270-3724)
Housen Li (ORCID: https://orcid.org/0000-0002-2434-9878)
Axel Munk (ORCID: https://orcid.org/0000-0002-9181-9331)

Institutions

Roche (Switzerland) (CH)
Institute of Molecular Bioimaging and Physiology (IT)
University of Göttingen (DE)

Publication Details

Journal: Journal of Computational and Graphical Statistics
Published: 2025-10-13
DOI: https://doi.org/10.1080/10618600.2025.2572328
Primary Topic: Algorithms and Data Compression
Type: article
Field-Weighted Citation Impact: 0.00

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article

Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models

Alexandre Mösching, Housen Li, Axel Munk

Oct 13, 2025

Journal of Computational and Graphical Statistics

Algorithms and Data Compression

article

Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models

Alexandre Mösching, Housen Li, Axel Munk

October 13, 2025article en

Abstract

No abstract available for this paper.

Journal of Computational and Graphical Statistics

Roche (Switzerland) (CH), Institute of Molecular Bioimaging and Physiology (IT), University of Göttingen (DE)

Openalex Percentile: Top 85%

Algorithms and Data Compression

DOI Open Access PDF OpenAlex

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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models — Alexandre Mösching, Housen Li, et al. · Journal of Computational and Graphical Statistics (2025) | TGRS Research Map | TGRS