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» On Bayesian model and variable selection using MCMC
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SYNTHESE
2008
130views more  SYNTHESE 2008»
14 years 9 months ago
Appropriateness measures: an uncertainty model for vague concepts
Abstract We argue that in the decision making process required for selecting assertible vague descriptions of an object, it is practical that communicating agents adopt an epistemi...
Jonathan Lawry
ICC
2009
IEEE
133views Communications» more  ICC 2009»
15 years 4 months ago
Reducing Average Power in Wireless Sensor Networks through Data Rate Adaptation
—The use of variable data rate can reduce network latency and average power consumption, and automatic rate selection is critical for improving scalability and minimizing network...
Steven Lanzisera, Ankur Mehta, Kristofer S. J. Pis...
CVPR
2003
IEEE
15 years 11 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman
BMCBI
2007
128views more  BMCBI 2007»
14 years 10 months ago
A Bayesian nonparametric method for prediction in EST analysis
Background: Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several sta...
Antonio Lijoi, Ramsés H. Mena, Igor Prü...
PAMI
2007
155views more  PAMI 2007»
14 years 9 months ago
Localization of Shapes Using Statistical Models and Stochastic Optimization
—In this paper, we present a new model for deformations of shapes. A pseudolikelihood is based on the statistical distribution of the gradient vector field of the gray level. The...
François Destrempes, Max Mignotte, Jean-Fra...