Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for ...

Markov decision processes (MDPs) and stochastic control constitute pivotal frameworks for modelling decision-making in systems subject to uncertainty. At their core, MDPs provide a structured means to ...

Randomness is inherent to real world problems so faculty research in this area includes the development and application of probabilistic tools to model, predict, and analyze randomness in applications ...

This paper introduces and explores variations on a natural extension of the intensity-based doubly stochastic framework for credit default. The essential addition proposed here is to introduce a ...