Olof Molander

Heavy-tail-aware representation learning and dynamic Bayesian state modelling to derive an operational proxy definition of problem gambling risk from routine online gambling data

Abstract Background: Problem gambling causes harm, but operational identification often relies on heuristic thresholds or sparse manual reviews. Routine online gambling logs are heavy-tailed and temporally structured, complicating risk definition and early detection. Methods: We...