30 search results - page 4 / 6 » Bayesian Monte Carlo for the Global Optimization of Expensiv... |
UAI
2000
13 years 7 months ago
2000
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
ANOR
2007
13 years 6 months ago
2007
We present a novel portfolio selection technique, which replaces the traditional maximization of the utility function with a probabilistic approach inspired by statistical physics....
CVPR
2000
IEEE
14 years 8 months ago
2000
IEEE
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
AE
2005
Springer
13 years 12 months ago
2005
Springer
Randomization is an efficient tool for global optimization. We here define a method which keeps : – the order 0 of evolutionary algorithms (no gradient) ; – the stochastic as...
GECCO
2004
Springer
13 years 11 months ago
2004
Springer
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlinear optimization problems. As its previous version, the approach does not require...