Learning in the absence of training data—A galactic application

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20035

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DC Field	Value	Language
dc.contributor.author	Spire, C	-
dc.contributor.author	Chakrabarty, D	-
dc.date.accessioned	2020-01-20T09:58:27Z	-
dc.date.available	2019-01-01	-
dc.date.available	2020-01-20T09:58:27Z	-
dc.date.issued	2019	-
dc.identifier.citation	Springer Proceedings in Mathematics and Statistics, 2019, 296 pp. 43 - 51	en_US
dc.identifier.isbn	9783030306106	-
dc.identifier.issn	2194-1009	-
dc.identifier.uri	https://bura.brunel.ac.uk/handle/2438/20035	-
dc.format.extent	43 - 51	-
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	Bayesian statistics and new generations	en_US
dc.subject	Bayesian learning	en_US
dc.subject	dark matter in galaxies	en_US
dc.subject	Metropolis-within-Gibbs	en_US
dc.subject	state space density	en_US
dc.title	Learning in the absence of training data—A galactic application	en_US
dc.type	Conference Paper	en_US
dc.identifier.doi	https://doi.org/10.1007/978-3-030-30611-3_5	-
dc.relation.isPartOf	Springer Proceedings in Mathematics and Statistics	-
pubs.publication-status	Published	-
pubs.volume	296	-
dc.identifier.eissn	2194-1017	-
dc.identifier.eissn	2194-1017	-
Appears in Collections:	Dept of Mathematics Research Papers

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