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» Explaining inferences in Bayesian networks
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SBACPAD
2006
IEEE
148views Hardware» more  SBACPAD 2006»
14 years 1 months ago
Scalable Parallel Implementation of Bayesian Network to Junction Tree Conversion for Exact Inference
We present a scalable parallel implementation for converting a Bayesian network to a junction tree, which can then be used for a complete parallel implementation for exact inferen...
Vasanth Krishna Namasivayam, Animesh Pathak, Vikto...
BMCBI
2010
229views more  BMCBI 2010»
13 years 7 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
AIED
2005
Springer
14 years 1 months ago
Inferring learning and attitudes from a Bayesian Network of log file data
A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and hel...
Ivon Arroyo, Beverly Park Woolf
IJAR
2000
140views more  IJAR 2000»
13 years 7 months ago
Belief updating in multiply sectioned Bayesian networks without repeated local propagations
Multiply sectioned Bayesian networks (MSBNs) provide a coherent and flexible formalism for representing uncertain knowledge in large domains. Global consistency among subnets in a...
Yang Xiang
AI
2010
Springer
13 years 7 months ago
Understanding the scalability of Bayesian network inference using clique tree growth curves
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
Ole J. Mengshoel