Report on stochastic process discovery by weight estimation experimental results

Many algorithms now exist for discovering process models from event logs. These models usually describe a control flow and are intended for use by people in analysing and improving real-world organizational processes. The relative likelihood of choices made while following a process is highly relevant information which few existing algorithms make available in their automatically discovered models. This can addressed by automatically discovered stochastic process models. This report presents more detailed experimental results related to a framework for automatic discovery of stochastic process models, detailed in a companion paper.