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    The random set approach to nontraditional measurements is rigorously Bayesian

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    El-Fallah, A.
    Date
    2012
    Collection
    • Curtin Research Publications
    Type
    Conference Paper
    Metadata
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    Abstract

    In several previous publications the first author has proposed a "generalized likelihood function" (GLF) approach for processing nontraditional measurements such as attributes, features, natural-language statements, and inference rules. The GLF approach is based on random set "generalized measurement models" for nontraditional measurements. GLFs are not conventional likelihood functions, since they are not density functions and their integrals are usually infinite, rather than equal to 1. For this reason, it has been unclear whether or not the GLF approach is fully rigorous from a strict Bayesian point of view. In a recent paper, the first author demonstrated that the GLF of a specific type of nontraditional measurement - quantized measurements - is rigorously Bayesian. In this paper we show that this result can be generalized to arbitrary nontraditional measurements, thus removing any doubt that the GLF approach is rigorously Bayesian. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

    Citation
    Mahler, R. and El-Fallah, A. 2012. The random set approach to nontraditional measurements is rigorously Bayesian.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    URI
    http://hdl.handle.net/20.500.11937/55323
    DOI
    10.1117/12.919824
    Department
    Department of Electrical and Computer Engineering

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