Because a number of uncertainties related to seismic hazard and physical vulnerability exist, it has been widely acknowledged that probabilistic approaches and methodological frameworks are required when assessing, in a prospective way, feasible future losses (McGuire 2004; Grossi and Kunreuther 2005; UNISDR 2013; GFDRR 2014).
This version is compared to the commonly used probabilistic approach from a methodological point of view.
This chapter describes methodological issues in both probabilistic and nonprobabilistic surveys, and describes the development process for surveys, including developing survey instruments, pilot testing surveys, and ensuring a sufficient response rate.
Firstly, de jure TLS methodological approach is a non probabilistic method and consequently it is not feasible to capture all the member of the target population [ 29].
This, along with the methodological difference related to the probabilistic modelling in our paper of the postponement of cases, might account for the difference between our findings and those of Jonsson and colleagues [ 17], who showed a minimal change in the excess of incidence after correction for lead time.
The proposed methodology constitutes a new methodological approach to solving the problems of risk assessment of the activities of CO2 geological storage (CGS), based on the determination of probabilistic risk assessment (PRA) through Bayesian networks (BNs) and Monte Carlo probabilities [9].
Future modelling of these policy choices should attempt to use data that allow more probabilistic sensitivity analysis to be conducted, but with the proviso that model structure (or methodological) uncertainty can only be explored using traditional 'non-probabilistic' methods.
Done with regard to methodological aspects of information retrieval and evaluation, and uncertainty associated with parameter precision (probabilistic sensitivity analysis).
E.g. the current methodological guidelines for economic evaluations in Hungary does not necessitate the use of probabilistic sensitivity analysis (PSA), once it becomes recommended, the checklist can be extended with new items on PSAs.
Ideal data mining methods for brain network analysis should be able to overcome these methodological problems by generalizing the network edges to positive and negative weighted cases, e.g., probabilistic weights in fMRI brain networks and integral weights in DTI brain networks.
