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Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.

Jan L Münch1, Ralf Schmauder1, Fabian Paul2

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Summary
This summary is machine-generated.

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.

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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.