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» The Measurable Space of Stochastic Processes
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ICML
1999
IEEE
14 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
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...
Sebastian Thrun, John Langford, Dieter Fox
INFORMS
1998
142views more  INFORMS 1998»
13 years 7 months ago
Distributed State Space Generation of Discrete-State Stochastic Models
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
MASCOTS
1996
13 years 8 months ago
Well-Defined Stochastic Petri Nets
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...
Gianfranco Ciardo, Robert Zijal
IRREGULAR
1997
Springer
13 years 11 months ago
Parallel Shared-Memory State-Space Exploration in Stochastic Modeling
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...
Susann C. Allmaier, Graham Horton
FORMATS
2006
Springer
13 years 11 months ago
A Characterization of Meaningful Schedulers for Continuous-Time Markov Decision Processes
Abstract. Continuous-time Markov decision process are an important variant of labelled transition systems having nondeterminism through labels and stochasticity through exponential...
Nicolás Wolovick, Sven Johr