Linked Life Data

9004379

Source:http://linkedlifedata.com/resource/pubmed/id/9004379

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1997-3-20
pubmed:abstractText
This paper studies the problems of inference and prediction in a class of models known as hierarchical mixtures-of-experts (HME). The statistical model underlying an HME is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. Bayesian inference regarding an HME's parameters is presented in the contexts of regression and classification using Markov chain Monte Carlo methods. A benefit of this Bayesian approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than provided by a point estimate. The methods are illustrated on a nonlinear regression problem and on a breast cancer classification problem. The results indicate that the HME showed good prediction performance, and also gave the additional benefit of providing for the opportunity to assess the degree of certainty of the model in its predictions.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0962-2802
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
375-90
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification.
pubmed:affiliation
Department of Brain and Cognitive Sciences, University of Rochester, New York 14627, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't