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Identifier 000412740
Title Coarse-graining of molecular systems through Bayesian statistics
Alternative Title Αδροποιημένα μοντέλα μοριακών συστημάτων χρησιμοποιώντας στατιστική κατα Bayesian
Author Καβουσανού, Σοφία-Ιωάννα
Thesis advisor Χαρμανδάρης, Βαγγέλης
Καλλιγιαννάκη, Ευαγγελία
Abstract The Bayesian inference is a statistical method applied to many problems in statistics, data analysis, statistical mechanics, etc., to reveal information from observations. The subject of the current thesis is to apply the empirical Bayes method to infer approximate coarse- grained models for molecular systems at equilibrium. Coarse-graining is a well established mathematical approach to reduce the dimensionality of a physical system. We study two variants of the empirical Bayes approach that enhance the force matching method. The first considers different deviations for each parameter of the coarse-grained interaction potential, while the second considers identical deviations but benefits on the computational effort. We develop a coarse-grained (CG) model for a system of methane molecules and apply the force matching method and the empirical Bayes approach. We examine two different linear representation functions for the coarse-grained pair interaction potential, the linear splines and the Lennard-Jones.
Language English
Issue date 2017-11-24
Collection   School/Department--School of Sciences and Engineering--Department of Mathematics and Applied Mathematics--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/d/3/2/metadata-dlib-1512024700-502033-7665.tkl Bookmark and Share
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