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Related Experiment Video

Updated: Sep 8, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

MAP segmentation in Bayesian hidden Markov models: a case study.

Alexey Koloydenko¹, Kristi Kuljus², Jüri Lember²

¹Royal Holloway, University of London, London, UK.

Journal of Applied Statistics

|June 16, 2022

Summary

This summary is machine-generated.

This study compares Bayesian and frequentist approaches for hidden Markov models (HMMs) in protein alignment. Bayesian methods offer a robust alternative when Viterbi algorithm is not applicable for maximum posterior probability (MAP) state sequence estimation.

Keywords:

Bayesian inference EM algorithm Hidden Markov model MAP sequence viterbi algorithm

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Last Updated: Sep 8, 2025

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Computational Biology
Bioinformatics
Statistical Modeling

Background:

Hidden Markov Models (HMMs) are widely used for sequence analysis.
Estimating the Maximum Posterior Probability (MAP) state sequence is crucial for HMM applications.
Bayesian approaches offer a principled framework for parameter estimation with prior information.

Purpose of the Study:

To evaluate Bayesian MAP segmentation for HMMs with Dirichlet priors against frequentist methods.
To investigate the application of Bayesian HMMs to protein alignment data.
To compare iterative algorithms for MAP path estimation in the Bayesian setup.

Main Methods:

Utilized a training set of thousands of protein alignment pairs.
Applied Dirichlet priors to emission and transition matrices for Bayesian HMMs.
Compared several iterative algorithms for MAP state sequence estimation, as Viterbi is inapplicable.
Contrasted Bayesian parameter estimation with frequentist approaches.

Main Results:

Demonstrated the feasibility of Bayesian MAP segmentation for HMMs in protein sequence analysis.
Identified and compared the performance of iterative algorithms for MAP path finding.
Provided a comparative analysis of Bayesian versus frequentist parameter estimation strategies.

Conclusions:

The Bayesian setup provides a viable alternative for HMM parameter estimation and MAP segmentation, especially when traditional methods are not directly applicable.
Iterative algorithms are effective for determining the MAP path in Bayesian HMMs.
This work contributes to the understanding of Bayesian inference in bioinformatics applications.