Coalescent experiments II: Markov bases of classical population genetic statistics

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dc.contributor.authorSainudiin, R.
dc.contributor.authorThornton, K.
dc.contributor.authorBooth, J.
dc.contributor.authorStillman, M.
dc.contributor.authorYoshida, R.
dc.date.accessioned2009-10-08T20:18:22Z
dc.date.available2009-10-08T20:18:22Z
dc.date.issued2009en
dc.description.abstractEvaluating the likelihood function of parameters in complex population genetic models from extant deoxyribonucleic acid (DNA) sequences is computationally prohibitive. In such cases, one may approximately infer the parameters from various summary statistics of the data. Such method are known as approximate likelihood/Bayesian computations. We employ computational commutative algebraic methods to obtain the exact likelihood of a large class of summary statistics that are linear combinations of the site frequency spectrum.en
dc.identifier.citationSainudiin, R., Thornton, K., Booth, J., Stillman, M., Yoshida, R. (2009) Coalescent experiments II: Markov bases of classical population genetic statistics. UCDMS Research Report 2009/8. 17pp..en
dc.identifier.urihttp://hdl.handle.net/10092/2950
dc.language.isoen
dc.publisherDepartment of Mathematics & Statisticsen
dc.publisherUniversity of Canterbury. Mathematics and Statisticsen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subjectintegrating controlled coalescent measures via Markov basesen
dc.subjectexactly approximate Bayesian/likelihood computation in population geneticsen
dc.subject.marsdenFields of Research::230000 Mathematical Sciences::239900 Other Mathematical Sciences::239901 Biological Mathematicsen
dc.subject.marsdenFields of Research::270000 Biological Sciences::270200 Geneticsen
dc.titleCoalescent experiments II: Markov bases of classical population genetic statisticsen
dc.typeReports
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