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© 2016 ISIF. This paper studies the problem of simultaneous deciding on hypotheses and estimating a random parameter. We propose a joint decision and estimation (JDE) formulation, which amounts to minimizing a risk related to both decision and estimation while decision performance is also constrained within a tolerable level. The risk used in this paper is a weighted sum of estimation costs conditioned on correct decisions times the correct decision probabilities. Such a risk allows us to focus on the joint (decision and estimation) performance pertinent to correct decisions only. The JDE solution includes handling two subproblems - estimation and decision. For the estimation subproblem, we derive the optimal Bayes estimator. For the decision subproblem, we provide the optimal solution via a numerical algorithm of a two-dimensional search. To reduce the complexity, we also provide a suboptimal solution when the decision rule is confined to the likelihood ratio tests. The suboptimal solution is implemented with a one-dimensional search in a finite range. Numerical results are provided to validate our methods.
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FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
Year: 2016
Publish Date: 2016-08-01
Page: 1284-1291
Language: English
Cited Count:
WoS CC Cited Count: 0
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ESI Highly Cited Papers on the List: 0 Unfold All
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Chinese Cited Count:
30 Days PV: 9
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