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» Inferring Network Invariants Automatically
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NIPS
2007
13 years 8 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
ICRA
2009
IEEE
94views Robotics» more  ICRA 2009»
14 years 2 months ago
Inferring a probability distribution function for the pose of a sensor network using a mobile robot
— In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometr...
David Meger, Dimitri Marinakis, Ioannis M. Rekleit...
WCET
2008
13 years 8 months ago
INFER: Interactive Timing Profiles based on Bayesian Networks
We propose an approach for timing analysis of software-based embedded computer systems that builds on the established probabilistic framework of Bayesian networks. We envision an ...
Michael Zolda
FMCAD
2008
Springer
13 years 9 months ago
Automatic Non-Interference Lemmas for Parameterized Model Checking
Parameterized model checking refers to any method that extends traditional, finite-state model checking to handle systems arbitrary number of processes. One popular approach to thi...
Jesse D. Bingham
ESOP
2009
Springer
14 years 2 months ago
An Interval-Based Inference of Variant Parametric Types
Abstract. Variant parametric types (VPT) represent the successful result of combining subtype polymorphism with parametric polymorphism to support a more flexible subtyping for Ja...
Florin Craciun, Wei-Ngan Chin, Guanhua He, Shengch...