This paper is concerned with developing an information-theoretic framework to aggregate the state space of a Hidden Markov Model (HMM) on discrete state and observation spaces. The...
The problem of discrete universal filtering, in which the components of a discrete signal emitted by an unknown source and corrupted by a known discrete memoryless channel (DMC) ar...
In this paper we present a novel performance analysis technique for large-scale systems modelled in the stochastic process algebra PEPA. In contrast to the well-known approach of ...
It is well known that among all probabilistic graphical Markov models the class of decomposable models is the most advantageous in the sense that the respective distributions can b...
This work presents a novel approach to object localization in complex imagery. In particular, the spatial extents of objects characterized by distinct spatial signatures at multip...