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 ...

Markov chains and queueing theory together provide a robust framework for analysing systems that evolve randomly over time. Markov chains describe stochastic processes where the future state depends ...

Models suitable for statistical inference in Markov chains are considered featuring various forms of stochastic entry, including Poisson, renewable binomial pool, uncertain pool size, negative ...

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 ...

CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...