Quantitative risk analysis data sources

Once all basic event probabilities are determined for a consequence tree the risk value can be computed. The top consequence probability is obtained by the classical approach of combining the basic event probabilities according to the minimal cut set equation. However, for the mathematical combination of probabilities, dependent uncertainty analysis is used. This analysis is such that the data sources determine the coupling of uncertainties and hence the uncertainty in the risk value. In addition, a quantitative subjectivity value is calculated for the top-consequence probability estimate based on the data subjectivities of the contributing basic events. 

Methods of numerical sensitivity and uncertainty analysis can be used to examine uncertainty and identify the key sources of bias and imprecision in quantitative estimates of risk. Once identified, limited resources (e.g., time, funding) can be efficiently allocated to obtain new information and data for those major sources of uncertainty and reduce it. These analyses can be repeated until uncertainties associated with the risk estimates are of an acceptable degree or until uncertainties cannot be further reduced. 

As already discussed in Chapter I in connection with risk matrix, in qualitative analysis, likelihood is estimated and categorized based on experience and judgment applicable for the project. Also these risk categorizations may he done on a quantitative basis as already discussed (say once in a year, etc.). In quantitative analysis the same is done based on previous records or a failure database for which quantitative PHA may be helpful. Failure occurrence data from other plants within or outside the company could be a good source of data. 

Evidence synthesis is a term used for synthesis of results from diverse sources and covers a wide range of analysis approaches (Sutton and Abrams, 2001). Bayesian Evidence Synthesis (here denoted as BES) is a statistical framework for exphcitly modeling several related and connected sources of data, in which uncertainty in model parameters are incorporated (Jackson et al., 2013). BES can be seen as a complex meta-analysis (Sutton and Abrams, 2001), where complex means to consider multiple effects from an intervention. Classical meta-analyses are usually based on studies that directly have observed the effect of an intervention. A broader view on meta-analyses allows for studies on effects on a lower level which are combined with quantitative modelling to assess the effect of an intervention on a higher level. In this view, a risk assessment can be seen as a meta-analysis (Linkov et al., 2009). Opening up for a quantitative assessment (or complex computer) model to measure effects, makes it possible to synthesize evidence for effects which are difficult, if at all, to empirically observe. In the PVA example, there is for example no possibility of... 

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