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dc.contributor.advisorKou, Samuel Samuel
dc.contributor.authorDu, Chao
dc.date.accessioned2013-02-21T21:55:34Z
dash.embargo.terms2014-06-21en_US
dc.date.issued2013-02-21
dc.date.submitted2012
dc.identifier.citationDu, Chao. 2012. Stochastic Modeling and Bayesian Inference with Applications in Biophysics. Doctoral dissertation, Harvard University.en_US
dc.identifier.otherhttp://dissertations.umi.com/gsas.harvard:10366en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10323651
dc.description.abstractThis thesis explores stochastic modeling and Bayesian inference strategies in the context of the following three problems: 1) Modeling the complex interactions between and within molecules; 2) Extracting information from stepwise signals that are commonly found in biophysical experiments; 3) Improving the computational efficiency of a non-parametric Bayesian inference algorithm. Chapter 1 studies the data from a recent single-molecule biophysical experiment on enzyme kinetics. Using a stochastic network model, we analyze the autocorrelation of experimental fluorescence intensity and the autocorrelation of enzymatic reaction times. This chapter shows that the stochastic network model is capable of explaining the experimental data in depth and further explains why the enzyme molecules behave fundamentally differently from what the classical model predicts. The modern knowledge on the molecular kinetics is often learned through the information extracted from stepwise signals in experiments utilizing fluorescence spectroscopy. Chapter 2 proposes a new Bayesian method to estimate the change-points in stepwise signals. This approach utilizes marginal likelihood as the tool of inference. This chapter illustrates the impact of the choice of prior on the estimator and provides guidelines for setting the prior. Based on the results of simulation study, this method outperforms several existing change-points estimators under certain settings. Furthermore, DNA array CGH data and single molecule data are analyzed with this approach. Chapter 3 focuses on the optional Polya tree, a newly established non-parametric Bayesian approach (Wong and Li 2010). While the existing study shows that the optional Polya tree is promising in analyzing high dimensional data, its applications are hindered by the high computational costs. A heuristic algorithm is proposed in this chapter, with an attempt to speed up the optional Polya tree inference. This study demonstrates that the new algorithm can reduce the running time significantly with a negligible loss of precision.en_US
dc.description.sponsorshipStatisticsen_US
dc.language.isoen_USen_US
dash.licenseMETA_ONLY
dc.subjectBayesian inferenceen_US
dc.subjectchange-pointsen_US
dc.subjectsingle-molecule experimenten_US
dc.subjectstepwise signalen_US
dc.subjectstochastic network modelen_US
dc.subjectstatisticsen_US
dc.subjectbiophysicsen_US
dc.subjectoptional Polya treeen_US
dc.titleStochastic Modeling and Bayesian Inference with Applications in Biophysicsen_US
dc.typeThesis or Dissertationen_US
dash.embargo.until10000-01-01
thesis.degree.date2012en_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorHarvard Universityen_US
thesis.degree.leveldoctoralen_US
thesis.degree.namePh.D.en_US
dc.contributor.committeeMemberMeng, Xiao-Lien_US
dc.contributor.committeeMemberBlitzstein, Josephen_US


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