Titlebook: Probabilistic Risk Analysis and Bayesian Decision Theory; Marcel van Oijen,Mark Brewer Book 2022 The Author(s), under exclusive license to

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Implementation of BDT Using Bayesian Networks,This chapter provides details on the implementation of BDT. As examples, we use similarly simple models as in Chap. ., but now show explicitly how we extract information from them.

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A Spatial Example: Forestry in Scotland,The main purpose of this example is to show how theory developed in earlier chapters can be applied to a spatial decision problem where model inputs and outputs are maps of a geographical region. We also intend this and the following chapters to be a repository of succinct and efficient R-algorithms for spatial problems in PRA and BDT.

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Three-Component PRA in the Spatial Example,We shall not show any details of a 3-component PRA here—there are no special problems—but just point out that R’s . package makes it easy to calculate a spatially aggregated map for ., defined as the fraction of an area that has experienced drought at least once in the observational record (see Chap. .).

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Marcel van Oijen,Mark BrewerIntroduces a new theory of probabilistic risk analysis that is rigorous and versatile.Explains how risk analysis is related to Bayesian decision theory.Provides many examples, all with R-code

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https://doi.org/10.1007/978-3-031-16333-3Bayesian Methods; Decision Theory; Hazards; Probability Theory; Risk Analysis; System Vulnerability; Uncer