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Proceedings Paper

The random set approach to nontraditional measurements is rigorously Bayesian
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Paper 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.

Paper Details

Date Published: 18 May 2012
PDF: 10 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920D (18 May 2012); doi: 10.1117/12.919824
Show Author Affiliations
Ronald Mahler, Lockheed Martin MS2 (United States)
Adel El-Fallah, Scientific Systems Co., Inc. (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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