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Abstract:
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Finding a subset collection that provides optimal population coverage is a frequently encountered deterministic problem. A random sample is often used to formulate the optimal coverage (OC) model, which is then used to select the subsets that provide the estimated optimal population coverage. Conceptualizing sample elements as counts in a contingency table, we have shown how decision-makers can combine prior information with sample data to help formulate OC models within a Bayesian framework. We now consider extensions that entail augmenting cell counts with random variables that potentiate, i.e., reflect potential for each element. For example, instead of focusing on the number of bank visits made by the families in a cell, an analyst might be interested in the total value of potential transactions to banks made by those families.
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