Marked point process framework for living probabilistic safety assessment and risk follow-up

We construct a model for living probabilistic safety assessment (PSA) by applying the general framework of marked point processes. The framework provides a theoretically rigorous approach for considering risk follow-up of posterior hazards. In risk follow-up, the hazard of core damage is evaluated synthetically at time points in the past, by using some observed events as logged history and combining it with re-evaluated potential hazards. There are several alternatives for doing this, of which we consider three here, calling them initiating event approach, hazard rate approach, and safety system approach. In addition, for a comparison, we consider a core damage hazard arising in risk monitoring. Each of these four definitions draws attention to a particular aspect in risk assessment, and this is reflected in the behaviour of the consequent risk importance measures. Several alternative measures are again considered. The concepts and definitions are illustrated by a numerical example.     

A Bayesian classification model for sea ice roughness fromscatterometer data

For sea ice in the Baltic Sea, surface scattering can be regarded as the dominant scattering mechanism at C-band. In this paper, a new statistical method is introduced for making statistical inferences about the underlying ice surface roughness on the basis of one-dimensional (1D) scatterometer data y. The central parameter in the hierarchical model applied in the context is a mixture parameter p, which indicates the degree of surface roughness in ice surface. Several questions related to the occurrence of different ice classes on a transect can be solved with the aid of the posterior distribution [p|y]. An empirical approximation for the posterior distribution is computed by using Markov Chain Monte Carlo methodology. The efficiency of the suggested approach is investigated by analyzing a C-band HH-polarization helicopter-borne HUTSCAT scatterometer data. The results provided by the statistical model show good agreement with a video-based ice type classification