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Scale Mixture Models with Applications to Bayesian Inference
dc.contributor.authorQin, Zhaohui Steveen_US
dc.contributor.authorDamien, Paulen_US
dc.contributor.authorWalker, Stephen G.en_US
dc.date.accessioned2011-11-15T16:02:17Z
dc.date.available2011-11-15T16:02:17Z
dc.date.issued2003-11-25en_US
dc.identifier.citationQin, Zhaohui S.; Damien, Paul; Walker, Stephen (2003). "Scale Mixture Models with Applications to Bayesian Inference." AIP Conference Proceedings 690(1): 394-395. <http://hdl.handle.net/2027.42/87496>en_US
dc.identifier.otherAPCPCS-690-1en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/87496
dc.description.abstractScale mixtures of uniform distributions are used to model non‐normal data in time series and econometrics in a Bayesian framework. Heteroscedastic and skewed data models are also tackled using scale mixture of uniform distributions. © 2003 American Institute of Physicsen_US
dc.publisherThe American Institute of Physicsen_US
dc.rights© The American Institute of Physicsen_US
dc.titleScale Mixture Models with Applications to Bayesian Inferenceen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/87496/2/394_1.pdf
dc.identifier.doi10.1063/1.1632162en_US
dc.identifier.sourceTHE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithmen_US
dc.owningcollnamePhysics, Department of


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