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Bayesian Object Identification: Variants

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  • Ritter, Gunter
  • Gallegos, María Teresa

Abstract

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.

Suggested Citation

  • Ritter, Gunter & Gallegos, María Teresa, 2002. "Bayesian Object Identification: Variants," Journal of Multivariate Analysis, Elsevier, vol. 81(2), pages 301-334, May.
    • Handle: RePEc:eee:jmvana:v:81:y:2002:i:2:p:301-334
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    References listed on IDEAS

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    1. Ritter, Gunter & Pesch, Christoph, 2001. "Polarity-free automatic classification of chromosomes," Computational Statistics & Data Analysis, Elsevier, vol. 35(3), pages 351-372, January.
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    Cited by:

    1. Amitrajeet A. Batabyal & Hamid Beladi, 2015. "Optimal Transport Provision To A Tourist Destination: A Mechanism Design Approach," Working Papers 0140eco, College of Business, University of Texas at San Antonio.
    2. Teresa Gallegos, María & Ritter, Gunter, 2006. "Parameter estimation under ambiguity and contamination with the spurious model," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1221-1250, May.
    3. Pamela C. Smith & Dana A. Forgione, 2008. "Global Outsourcing of Healthcare: A Medical Tourism Decision Model," Working Papers 0033, College of Business, University of Texas at San Antonio.
    4. Anuradha Roy & Ricardo Leiva, 2008. "An Extension of the Traditional Classi cation Rules: the Case of Non-Random Samples," Working Papers 0057, College of Business, University of Texas at San Antonio.
    5. Anuradha Roy & Ricardo Leiva, 2008. "Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data," Working Papers 0037, College of Business, University of Texas at San Antonio.
    6. Kihoon Yoon & Daijin Ko & Carolina B. Livi & Nathan Trinklein & Mark Doderer & Stephen Kwek & Luiz O. F. Penalva, 2008. "Over-represented sequences located on UTRs are potentially involved in regulatory functions," Working Papers 0053, College of Business, University of Texas at San Antonio.

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    1. Teresa Gallegos, María & Ritter, Gunter, 2006. "Parameter estimation under ambiguity and contamination with the spurious model," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1221-1250, May.

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