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Bayesian Object Identification: Variants

✍ Scribed by Gunter Ritter; Marı́a Teresa Gallegos

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
219 KB
Volume
81
Category
Article
ISSN
0047-259X

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✦ Synopsis

We present a Bayesian theory of object identification. Here, identifying an object means selecting a particular observation from a group of observations (variants), this observation (the regular variant) being characterized by a distributional model. In this sense, object identification means assigning a given model to one of several observations. Often, it is the statistical model of the regular variant, only, that is known. We study an estimator which relies essentially on this model and not on the characteristics of the ''irregular'' variants. In particular, we investigate under what conditions this variant selector is optimal. It turns out that there is a close relationship with exchangeability and Markovian reversibility. We finally apply our theory to the case of irregular variants generated from the regular variant by a Gaussian linear model.

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