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Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), in Journal of Machine Learning Research: W&CP vol. 9

dc.contributor.authorMurray, Iain
dc.contributor.authorPrescott Adams, Ryan
dc.contributor.authorMacKay, David J. C.
dc.date.accessioned2011-01-13T10:56:54Z
dc.date.available2011-01-13T10:56:54Z
dc.date.issued2010en
dc.identifier.issn1532-4435en
dc.identifier.urihttp://jmlr.csail.mit.edu/proceedings/papers/v9/murray10a.htmlen
dc.identifier.urihttp://hdl.handle.net/1842/4581
dc.description.abstractMany probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.en
dc.language.isoenen
dc.titleElliptical slice samplingen
dc.typeConference Paperen
rps.volume9en
rps.titleProceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), in Journal of Machine Learning Research: W&CP vol. 9en
dc.extent.noOfPages541-548en
dc.extent.pageNumbers541--548en
dc.date.updated2011-01-13T10:56:55Z
dc.identifier.eIssn1938-7228en
dc.date.openingDate2010-05-13
dc.date.closingDate2010-05-15


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