Advances on priors for the Dirichlet process mixture model with Gaussian kernels

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Description:: Complete version (A folder that contains two tex files (one contains the main thesis, one contains the glossaries), one bib file and one folder for pictures)

dc.contributor.advisor	Papathomas, Michail
dc.contributor.advisor	Thomas, Len
dc.contributor.author	Jing, Wei
dc.coverage.spatial	v, 249 p.	en_US
dc.date.accessioned	2022-11-03T15:42:19Z
dc.date.available	2022-11-03T15:42:19Z
dc.date.issued	2022-11-29
dc.identifier.uri	https://hdl.handle.net/10023/26299
dc.description.abstract	Abstract redacted	en_US
dc.description.sponsorship	"This work was supported by the Centre for Research into Ecological & Environmental Modelling (CREEM); and the School of Mathematics and Statistics; and the St Leonard's Postgraduate College." -- Funding	en
dc.language.iso	en	en_US
dc.title	Advances on priors for the Dirichlet process mixture model with Gaussian kernels	en_US
dc.type	Thesis	en_US
dc.contributor.sponsor	University of St Andrews. Centre for Research into Ecological and Environmental Modelling (CREEM)	en_US
dc.contributor.sponsor	University of St Andrews. School of Mathematics and Statistics	en_US
dc.contributor.sponsor	University of St Andrews. St Leonard's College	en_US
dc.type.qualificationlevel	Doctoral	en_US
dc.type.qualificationname	PhD Doctor of Philosophy	en_US
dc.publisher.institution	The University of St Andrews	en_US
dc.rights.embargodate	2024-06-21
dc.rights.embargoreason	Thesis restricted in accordance with University regulations. Restricted until 21st June 2024	en
dc.identifier.doi	https://doi.org/10.17630/sta/218