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» A Guided Monte Carlo Approach to Optimization Problems
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IPSN
2004
Springer
15 years 3 months ago
A probabilistic approach to inference with limited information in sensor networks
We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques. This capability is useful in that it allows...
Rahul Biswas, Sebastian Thrun, Leonidas J. Guibas
ENC
2005
IEEE
15 years 3 months ago
Saving Evaluations in Differential Evolution for Constrained Optimization
Generally, evolutionary algorithms require a large number of evaluations of the objective function in order to obtain a good solution. This paper presents a simple approach to sav...
Efrén Mezura-Montes, Carlos A. Coello Coell...
ICIP
2008
IEEE
15 years 4 months ago
Blind restoration of blurred photographs via AR modelling and MCMC
We propose a new image and blur prior model, based on nonstationary autoregressive (AR) models, and use these to blindly deconvolve blurred photographic images, using the Gibbs sa...
Tom E. Bishop, Rafael Molina, James R. Hopgood
BMVC
2000
14 years 11 months ago
Parallel Chains, Delayed Rejection and Reversible Jump MCMC for Object Recognition
We tackle the problem of object recognition using a Bayesian approach. A marked point process [1] is used as a prior model for the (unknown number of) objects. A sample is generat...
M. Harkness, P. Green
WSC
1997
14 years 11 months ago
Modeling Compressed Full-Motion Video
This paper presents a general approach to modeling VBR (variable bit rate) compressed full-motion video. The salient feature of such video is the existence of scenes. Scene struct...
Benjamin Melamed