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» Explaining inferences in Bayesian networks
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AAAI
2006
13 years 9 months ago
Solving MAP Exactly by Searching on Compiled Arithmetic Circuits
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely states of a set of variables given partial evidence on the complement of that set...
Jinbo Huang, Mark Chavira, Adnan Darwiche
BIBM
2008
IEEE
217views Bioinformatics» more  BIBM 2008»
14 years 2 months ago
Combining Hierarchical Inference in Ontologies with Heterogeneous Data Sources Improves Gene Function Prediction
The study of gene function is critical in various genomic and proteomic fields. Due to the availability of tremendous amounts of different types of protein data, integrating thes...
Xiaoyu Jiang, Naoki Nariai, Martin Steffen, Simon ...
ICANN
2010
Springer
13 years 5 months ago
Learning in a Unitary Coherent Hippocampus
Abstract. A previous paper [2] presented a model (UCPF-HC) of the hippocampus as a unitary coherent particle filter, which combines the classical hippocampal roles of associative m...
Charles W. Fox, Tony J. Prescott
ICIP
2003
IEEE
14 years 29 days ago
Image classification using multimedia knowledge networks
This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discove...
Ana B. Benitez, Shih-Fu Chang
UAI
2001
13 years 9 months ago
A Bayesian Multiresolution Independence Test for Continuous Variables
In this paper we present a method of computing the posterior probability of conditional independence of two or more continuous variables from data, examined at several resolutions...
Dimitris Margaritis, Sebastian Thrun