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Bayesian inference with misspecified models

✍ Scribed by Walker, Stephen G.

Book ID: 120373042
Publisher: Elsevier Science
Year: 2013
Tongue: English
Weight: 505 KB
Volume: 143
Category: Article
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2013.05.013

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Let Y denote an n x 1 vector of observations such that Y = # + a~ where /~ is an unknown n x 1 vector, a > 0 is an unknown parameter, and ~ is an n x 1 vector of independent standard normal random variables. A linear regression analysis is often based on a model for # such as /t =X/~ where X is a kn