Monte Carlo methods and Markov Chain algorithms have long been central to computational science, forming the backbone of numerical simulation in a variety of disciplines. These techniques employ ...

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 ...

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 ...

Markov Chain Monte Carlo (MCMC) methods have become indispensable in contemporary statistical science, enabling researchers to approximate complex probability distributions that are otherwise ...