A Bayesian Approach to Analyzing Uncertainty Among Stochastic Models

Wood EF (1974). A Bayesian Approach to Analyzing Uncertainty Among Stochastic Models. IIASA Research Report. IIASA, Laxenburg, Austria: RR-74-016

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Abstract

The statistical uncertainty, resulting from the lack of knowledge of which modeling represents a given stochastic process, is analyzed. This analysis of model uncertainty leads to a composite Bayesian distribution. The composite Bayesian distribution is a linear model of the individual Bayesian probability distributions of the individual models, weighted by the posterior probability that a particular model is the true model. The composite Bayesian probability model accounts for all sources of statistical uncertainty -- both parameter uncertainty and model uncertainty. This model is the one that should be used in applied problems of decision analysis, for it best represents the knowledge -- or lack of it -- to the decision maker about future events of the process.

Item Type: Monograph (IIASA Research Report)
Research Programs: System and Decision Sciences - Core (SDS)
Resources and Environment Area (REN)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 01:40
Last Modified: 21 Jul 2016 19:27
URI: http://pure.iiasa.ac.at/78

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