The ability to model cognitive agents depends crucially on being able to encode and infer with contextual information at many levels (such as situational, psychological, social, or...
Srini Narayanan, Katie Sievers, Steven J. Maiorano
In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the Stochastic v...
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
Probabilistic timed automata, a variant of timed automata extended with discrete probability distributions, is a specification formalism suitable for describing both nondeterminis...
Marta Z. Kwiatkowska, Gethin Norman, David Parker,...
Abstract. The technique of partial order reduction (POR) for probabilistic model checking prunes the state space of the model so that a maximizing scheduler and a minimizing one pe...