Mitchell's notion of representation independence is a particularly useful application of Reynolds' relational parametricity -- two different implementations of an abstra...
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a R...
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
Despite considerable effort, the state-space explosion problem remains an issue in the analysis of Markov models. Given structure, symbolic representations can result in very comp...
Marta Z. Kwiatkowska, Rashid Mehmood, Gethin Norma...