Bayesian cluster analysis with applications to pathogen population genomics

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dc.contributor Helsingin yliopisto, matemaattis-luonnontieteellinen tiedekunta, matematiikan ja tilastotieteen laitos fi
dc.contributor Helsingfors universitet, matematisk-naturvetenskapliga fakulteten, matematiska och statistiska institutionen sv
dc.contributor University of Helsinki, Faculty of Science, Department of Mathematics and Statistics en
dc.contributor.author Pessia, Alberto fi
dc.date.accessioned 2017-10-02T10:59:16Z
dc.date.available 2017-10-17 fi
dc.date.available 2017-10-02T10:59:16Z
dc.date.issued 2017-10-27 fi
dc.identifier.uri URN:ISBN:978-951-51-3676-3 fi
dc.identifier.uri http://hdl.handle.net/10138/224700
dc.description.abstract Identifying similarity patterns in heterogeneous observations is a very common problem in many branches of science. When the similarities and dissimilarities are encoded by a group structure, the task of dividing the observed sample into an unknown number of homogeneous groups is known as cluster analysis. Among the many types of statistical data analyses, it is one of the most widely applied. In evolutionary biology, for example, the population structure plays an important role. Groups naturally arise as the result of evolutionary processes and depending on the resolution of the study, clusters might represent similar molecules, organisms, or even species. With the huge amount of genetic data now freely available in on-line databases, cluster analysis is a valuable technique to better understand the evolution of organisms. In this dissertation we focus our attention on Bayesian approaches to model-based clustering. We review the mathematical formalization of the two most common methods, finite mixture models and product partition models, together with algorithms needed to draw inferences. We then introduce a novel Bayesian model which has been specifically designed to partition categorical data matrices. Finally, we show how cluster analysis is a very effective method for understanding the evolution of pathogens, and how this information is relevant to public health. en
dc.description.abstract - fi
dc.format.mimetype application/pdf fi
dc.language.iso en fi
dc.publisher Helsingin yliopisto fi
dc.publisher Helsingfors universitet sv
dc.publisher University of Helsinki en
dc.relation.isformatof URN:ISBN:978-951-51-3675-6 fi
dc.relation.isformatof Helsinki: Unigrafia, 2017 fi
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. sv
dc.subject fi
dc.title Bayesian cluster analysis with applications to pathogen population genomics en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Doktorsavhandling (sammanläggning) sv
dc.ths Corander, Jukka fi
dc.opn Consonni, Guido fi
dc.type.dcmitype Text fi

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