Scalable Inference for Hybrid Bayesian Hidden Markov Model Using Gaussian Process Emission

DOI10.1080/10618600.2021.2023021OpenAlexW4205990930MaRDI QIDQ5057079

Yohan Jung, Jinkyoo Park

Publication date: 15 December 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2021.2023021

zbMATH Keywords

Gaussian processstochastic variational inferencehybrid hidden Markov modelrandom Fourier featurescalable approximate Bayesian inferencespectral mixture kernel

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• ElemStatLearn
• HMMmix
• Adam

Cites Work

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• Selecting hidden Markov model state number with cross-validated likelihood
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• Algorithms for the Assignment and Transportation Problems
• Lectures on Fourier Integrals. (AM-42)
• Hierarchical Dirichlet Processes
• Nonparametric inference in hidden Markov models using P‐splines
• The Application of Hidden Markov Models in Speech Recognition
• Bayesian Methods for Hidden Markov Models
• Hidden Markov Models With Stick-Breaking Priors
• Advanced Lectures on Machine Learning
• The Analysis of Time Series
• Discrete-time signal processing. An algebraic approach
• Hidden Markov models with state-dependent mixtures: minimal representation, model testing and applications to clustering

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