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AI
2010
Springer
13 years 9 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
AI
2002
Springer
13 years 8 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
ICRA
2008
IEEE
150views Robotics» more  ICRA 2008»
14 years 3 months ago
A Bayesian approach to empirical local linearization for robotics
— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics system...
Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, St...
SAINT
2005
IEEE
14 years 2 months ago
On Scalable Modeling of TCP Congestion Control Mechanism for Large-Scale IP Networks
In this paper, we propose an analytic approach of modeling a closed-loop network with multiple feedback loops using fluid-flow approximation. Specifically, we model building bl...
Hiroyuki Ohsaki, Juñya Ujiie, Makoto Imase
TIP
2008
126views more  TIP 2008»
13 years 8 months ago
Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation
Abstract--In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constrain...
Hunsop Hong, Dan Schonfeld