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CJ
2010
131views more  CJ 2010»
14 years 7 months ago
Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks
an be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The...
Duncan Gillies, David Thornley, Chatschik Bisdikia...
CVBIA
2005
Springer
15 years 3 months ago
Registration of PET and MR Hand Volumes Using Bayesian Networks
Abstract. A method for the non-rigid, multi-modal, registration of volumetric scans of human hands is presented. PET and MR scans are aligned by optimising the configuration of a ...
Derek R. Magee, Steven Tanner, Michael Waller, Den...
81
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AAAI
1998
14 years 11 months ago
Structured Representation of Complex Stochastic Systems
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Nir Friedman, Daphne Koller, Avi Pfeffer
ICASSP
2011
IEEE
14 years 1 months ago
Lexical access experiments with context-dependent articulatory feature-based models
We address the problem of pronunciation variation in conversational speech with a context-dependent articulatory featurebased model. The model is an extension of previous work usi...
Preethi Jyothi, Karen Livescu, Eric Fosler-Lussier
BMCBI
2010
229views more  BMCBI 2010»
14 years 9 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck