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On Bayesian inference with conjugate priors for scale mixtures of normal distributions

Ng, V.M. (2010) On Bayesian inference with conjugate priors for scale mixtures of normal distributions. Journal of Applied Probability & Statistics, 5 (1). pp. 69-76.

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Bayesian inference is considered for the multivariate regression model with distribution of the random responses belonging to the multivariate scale mixtures of normal distributions. The posterior distribution of the regression parameters and the predictive distribution of future responses for the model are derived when the prior distribution of the parameters is from the conjugate family and they are shown to be identical to those obtained under normally distributed random responses. This gives inference robustness with respect to departures from the reference case of independent sampling from the normal distribution.

Item Type: Journal Article
Murdoch Affiliation(s): School of Chemical and Mathematical Science
Publisher: ISOSS Publications
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