Deriving the probability of a linear opinion pooling method being superior to a set of alternatives
| Autor: | Donnacha Bolger, Brett Houlding |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Mathematical optimization 021103 operations research Computer science Bayesian probability Pooling 0211 other engineering and technologies Probabilistic logic Contrast (statistics) 02 engineering and technology computer.software_genre Industrial and Manufacturing Engineering Set (abstract data type) Range (mathematics) Metric (mathematics) Data mining Safety Risk Reliability and Quality computer Decision analysis |
| Zdroj: | Reliability Engineering & System Safety. 158:41-49 |
| ISSN: | 0951-8320 |
| DOI: | 10.1016/j.ress.2016.10.008 |
| Popis: | Linear opinion pools are a common method for combining a set of distinct opinions into a single succinct opinion, often to be used in a decision making task. In this paper we consider a method, termed the Plug-in approach, for determining the weights to be assigned in this linear pool, in a manner that can be deemed as rational in some sense, while incorporating multiple forms of learning over time into its process. The environment that we consider is one in which every source in the pool is herself a decision maker (DM), in contrast to the more common setting in which expert judgments are amalgamated for use by a single DM. We discuss a simulation study that was conducted to show the merits of our technique, and demonstrate how theoretical probabilistic arguments can be used to exactly quantify the probability of this technique being superior (in terms of a probability density metric) to a set of alternatives. Illustrations are given of simulated proportions converging to these true probabilities in a range of commonly used distributional cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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