Degree Project in Molecular Biotechnology
Masters Programme in Molecular Biotechnology Engineering, Uppsala University School of Engineering
UPTEC X 15 001 Date of issue 2015-01 Author
Emil Marklund
Title (English)
Bayesian inference in aggregated hidden Markov models
Title (Swedish)
Abstract
Single molecule experiments study the kinetics of molecular biological systems. Many such studies generate data that can be described by aggregated hidden Markov models, whereby there is a need of doing inference on such data and models. In this study, model selection in aggregated Hidden Markov models was performed with a criterion of maximum Bayesian evidence. Variational Bayes inference was seen to underestimate the evidence for aggregated model fits. Estimation of the evidence integral by brute force Monte Carlo integration
theoretically always converges to the correct value, but it converges in far from tractable time.
Nested sampling is a promising method for solving this problem by doing faster Monte Carlo integration, but it was here seen to have difficulties generating uncorrelated samples.
Keywords
Bayesian inference, aggregated hidden Markov models, model selection, variational Bayes, nested sampling, single molecule data
Supervisors
Dr. Martin Lindén Uppsala University
Scientific reviewer
Prof. Mats Gustafsson Uppsala University
Project name Sponsors
Language
English
Security
ISSN 1401-2138 Classification
Supplementary bibliographical information Pages
54
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