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JMLR
2010
137views more  JMLR 2010»
14 years 11 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
GECCO
2006
Springer
165views Optimization» more  GECCO 2006»
15 years 8 months ago
Comparing mathematical models on the problem of network inference
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different mathemat...
Christian Spieth, Nadine Hassis, Felix Streichert
GLOBECOM
2010
IEEE
15 years 2 months ago
Cognitive Network Inference through Bayesian Network Analysis
Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios ...
Giorgio Quer, Hemanth Meenakshisundaram, Tamma Bhe...
133
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RECOMB
2000
Springer
15 years 8 months ago
Using Bayesian networks to analyze expression data
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the c...
Nir Friedman, Michal Linial, Iftach Nachman, Dana ...
120
Voted
AUSAI
2005
Springer
15 years 10 months ago
Conditioning Graphs: Practical Structures for Inference in Bayesian Networks
Abstract. Programmers employing inference in Bayesian networks typically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inf...
Kevin Grant, Michael C. Horsch