Related Experiment Video
Updated: Sep 15, 2025
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
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Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.
Jan L Münch1, Ralf Schmauder1, Fabian Paul2
1Institute of Physiology II, Jena University Hospital, Friedrich Schiller University, Jena 07743, Germany.
Biorxiv : the Preprint Server for Biology
|July 14, 2025
Summary
Bayesian inference with carefully chosen priors improves Hidden Markov models (HMMs) for biomolecules. This approach enhances accuracy and reduces uncertainty, even with low-quality data.
Area of Science:
- Computational biology
- Biophysics
- Statistical modeling
Background:
- Hidden Markov models (HMMs) are crucial for analyzing biomolecular data, but parameter non-identifiability hinders accurate inference.
- Both maximum likelihood and Bayesian inference methods face challenges due to these model complexities.
Purpose of the Study:
- To investigate the impact of prior distributions on Bayesian inference for HMMs in the context of parameter non-identifiability.
- To optimize inference for patch clamp data from ligand-gated ion channels.
Main Methods:
- Applied Bayesian inference with a focus on minimally informative prior distributions.
- Investigated the effect of confining parameter space to physically motivated limits.
- Incorporated assumptions of finite cooperativity for ligand-binding events.
Main Results:
- Minimally informative priors increase inference accuracy and decrease uncertainty.
- Stronger prior assumptions, like physically motivated limits, ensure a sufficiently proper posterior for complex HMMs.
- Finite cooperativity priors bias towards non-cooperativity while allowing data-driven inference.
Conclusions:
- Prior distributions are essential for robust Bayesian inference in HMMs with non-identifiable parameters.
- The proposed prior strategies enable meaningful inferences even with significantly lower quality datasets.
- This work advances the application of HMMs in biophysical modeling and data analysis.
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