Over the last two decades a considerable amount of effort has been put in the development and application of matrix geometric techniques for the analysis of queueing systems of wh...
Boudewijn R. Haverkort, Aad P. A. van Moorsel, Dir...
An increasing number of planners can handle uncertainty in the domain or in action outcomes. However, less work has addressed building plans when the planner's world can chan...
We consider a new simulation-based optimization method called the Nested Partitions (NP) method. This method generates a Markov chain and solving the optimization problem is equiv...
In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...
In this paper, we describe the dual-processor parallelisation of a symbolic (BDD-based) implementation of probabilistic model checking. We use multi-terminal BDDs, which allow a c...
Marta Z. Kwiatkowska, David Parker, Yi Zhang, Rash...
Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these...
We introduce relational grams (r-grams). They upgrade n-grams for modeling relational sequences of atoms. As n-grams, r-grams are based on smoothed n-th order Markov chains. Smoot...
Abstract. This paper shows that we can take advantage of information about the probabilities of the occurrences of events, when this information is available, to refine the classic...
Staffing and scheduling optimization in large multiskill call centers is time-consuming, mainly because it requires lengthy simulations to evaluate performance measures and their ...
This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probabili...