Bayesian nonparametric estimation of Milky Way parameters using matrix-variate data, in a new Gaussian Process based method

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20506

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DC Field	Value	Language
dc.contributor.author	Chakrabarty, D	-
dc.contributor.author	Biswas, M	-
dc.contributor.author	Bhattacharya, S	-
dc.date.accessioned	2020-03-12T15:06:38Z	-
dc.date.available	2015-01-01	-
dc.date.available	2020-03-12T15:06:38Z	-
dc.date.issued	2015	-
dc.identifier.citation	Electronic Journal of Statistics, 2015, 9 pp. 1378 - 1403	en_US
dc.identifier.citation	arXiv:1304.5967v4 [stat.AP]	-
dc.identifier.issn	1935-7524	-
dc.identifier.uri	https://bura.brunel.ac.uk/handle/2438/20506	-
dc.identifier.uri	https://arxiv.org/abs/1304.5967	-
dc.format.extent	1378 - 1403	-
dc.language.iso	en	en_US
dc.publisher	The Institute of Mathematical Statistics and the Bernoulli Society	en_US
dc.subject	supervised learning	en_US
dc.subject	inverse problems	en_US
dc.subject	Gaussian Process	en_US
dc.subject	matrix-variate normal	en_US
dc.subject	transformation-based MCMC	en_US
dc.title	Bayesian nonparametric estimation of Milky Way parameters using matrix-variate data, in a new Gaussian Process based method	en_US
dc.type	Article	en_US
dc.identifier.doi	https://doi.org/10.1214/15-EJS1037	-
dc.relation.isPartOf	Electronic Journal of Statistics	-
pubs.publication-status	Published	-
pubs.volume	9	-
Appears in Collections:	Dept of Mathematics Research Papers

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