We provide a short overview of Importance Sampling – a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations. We review the fundamental developments in designing efficient IS for practical use. This includes parametric approximation with optimization based adaptation, sequential sampl...

Abstract The junction-tree representation provides an attractive structural property for organising a decomposable graph . In this study, we present two novel stochastic algorithms, referred to as the expander and collapser , sequential sampling of junction trees graphs. We show that recursive application expander, which expands incrementally underlying with one vertex at time, has full support...

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate scenarios beyond historical records. Generating operating conditions is highly relevant the uncertain future of emerging power systems that are completely different systems. In response, first time, this work proposes novel split-based sequential sampling approach based on optimisation ge...