Bayesian Inference for Spatio-Temporal Models

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http://urn.fi/URN:ISBN:978-951-51-2290-2
Title: Bayesian Inference for Spatio-Temporal Models
Author: Shubin, Mikhail
Contributor: University of Helsinki, Faculty of Science, Department of Mathematics and Statistics
Publisher: Helsingin yliopisto
Date: 2016-06-29
URI: http://urn.fi/URN:ISBN:978-951-51-2290-2
http://hdl.handle.net/10138/163767
Thesis level: Doctoral dissertation (article-based)
Abstract: The dissertation presents five problem-driven research articles, representing three research domains related to micro-organisms causing infectious disease. Articles I and II are devoted to the A(H1N1)pdm09 influenza (`swine flu') epidemic in Finland 2009-2011. Articles III and IV present software tools for analysing experimental data produced by Biolog phenotype microarrays. Article V studies a mismatch distribution as a summary statistic for the inference about evolutionary dynamics and demographic processes in bacterial populations. All addressed problems share the following two features: (1) they concern a dynamical process developing in time and space; (2) the observations of the process are partial and imprecise. The problems are generally approached using Bayesian Statistics as a formal methodology for learning by confronting hypothesis to evidence. Bayesian Statistics relies on modelling: constructing a generative algorithm mimicking the object, process or phenomenon of interest.
Subject: statistics
Rights: This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.


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