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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
AUTOMATICA
2002
83views more  AUTOMATICA 2002»
13 years 7 months ago
A time aggregation approach to Markov decision processes
We propose a time aggregation approach for the solution of in
Xi-Ren Cao, Zhiyuan Ren, Shalabh Bhatnagar, Michae...
QEST
2009
IEEE
14 years 2 months ago
Nondeterministic Labeled Markov Processes: Bisimulations and Logical Characterization
We extend the theory of labeled Markov processes with internal nondeterminism, a fundamental concept for the further development of a process theory with abstraction on nondetermi...
Pedro R. D'Argenio, Nicolás Wolovick, Pedro...
AAAI
2007
13 years 9 months ago
Purely Epistemic Markov Decision Processes
Planning under uncertainty involves two distinct sources of uncertainty: uncertainty about the effects of actions and uncertainty about the current state of the world. The most wi...
Régis Sabbadin, Jérôme Lang, N...
CORR
2008
Springer
103views Education» more  CORR 2008»
13 years 7 months ago
Quickest Change Detection of a Markov Process Across a Sensor Array
Recent attention in quickest change detection in the multi-sensor setting has been on the case where the densities of the observations change at the same instant at all the sensor...
Vasanthan Raghavan, Venugopal V. Veeravalli