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Model discrimination: Posterior probabilities
Author(s) -
Dunne Adrian,
Lacey Laurence
Publication year - 1984
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450620413
Subject(s) - homoscedasticity , heteroscedasticity , posterior probability , mathematics , statistics , variance (accounting) , computation , econometrics , probability density function , algorithm , economics , bayesian probability , accounting
Expressions were derived for the computation of regression model posterior probabilities under a number of assumptions regarding the form of the uniform parameter prior probability density function. It was demonstrated that some of these forms may lead to a bias in the model posterior probabilities. The variance known and unknown situations were considered under both homoscedastic and heteroscedastic conditions. Bias of the model posterior probabilities may partly explain the unstable behaviour reported by previous workers.