In this paper, we present a patch-based variational Bayesian framework of image processing using the language of factor graphs (FGs). The variable and factor nodes of FGs represen...
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
We develop a framework for obtaining Fully Polynomial Time Approximation Schemes (FPTASs) for stochastic univariate dynamic programs with either convex or monotone single-period c...
Nir Halman, Diego Klabjan, Chung-Lun Li, James B. ...
Stochastic Petri nets (SPNs) have proven to be a powerful and enduring graphically-oriented framework for modelling and performance analysis of complex systems. This tutorial focu...
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...