We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models often requires the generatio...
Gianfranco Ciardo, Joshua Gluckman, David M. Nicol
Formalisms based on stochastic Petri Nets (SPNs) can employ structural analysis to ensure that the underlying stochastic process is fully determined. The focus is on the detection...
Stochastic modeling forms the basis for analysis in many areas, including biological and economic systems, as well as the performance and reliability modeling of computers and comm...
Abstract. Continuous-time Markov decision process are an important variant of labelled transition systems having nondeterminism through labels and stochasticity through exponential...