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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含糊其辭

忍耐

PRA vs. BDT in the Spatial Example,use irrigation in those locations where it increases the expectation for utility .[.], calculated from the 19 years of data. We aim to show that locations where irrigation increases .[.] are also the locations where it decreases the ... that we defined in Chap. ..

Adenoma

易受刺激

Sampling-Based Single-Threshold PRA,es .[.] are then replaced by normalised frequencies, but we shall denote those with .[.] too. Say the total number of (., .) observations is . of which .. have .-values that are below a threshold .. We then write, as before, .[.?

LOPE

temperate

Myosin

Sampling-Based Single-Threshold PRA: Uncertainty Quantification (UQ),Uncertainty in sampling-based expectations may be quantified as the standard deviations of the sample means, see Eq. (4.1).

ectropion

Density Estimation to Move from Sampling- to Distribution-Based PRA,Our approach to sampling-based single-threshold PRA with UQ in Chap. . was to take a collection of . data pairs (., .), define a hazardous region ., and estimate .[.], .? and . as, respectively, .∕.., . and .. The approach was completed with UQ using Eqs. (.)–(.).

殺死

Bayesian Model-Based PRA,In practice, given a particular data set we will want to model the relationship between outcome/response variables and associated explanatory variables. We can then use this model to conduct our PRA. We will illustrate this using the existing linear (bivariate Gaussian) and nonlinear examples.