Markov chain models and phase-type distributions have emerged as powerful tools in healthcare analytics, offering a robust framework for understanding and predicting patient trajectories throughout ...

High-order Markov chain models extend the conventional framework by incorporating dependencies that span several previous states rather than solely the immediate past. This extension allows for a ...

This paper deals with discrete-time Markov decision processes (MDPs) under constraints where all the objectives have the same form of expected total cost over the infinite time horizon. The existence ...

We consider a class of discrete time, dynamic decision-making models which we refer to as Periodically Time-Inhomogeneous Markov Decision Processes (PTMDPs). In these models, the decision-making ...

The ability to deal with unseen objects in a zero-shot manner makes machine learning models very attractive for applications in robotics, allowing robots to enter previously unseen environments and ...