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» On Using Monte Carlo Methods for Scheduling
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ML
2002
ACM
220views Machine Learning» more  ML 2002»
13 years 8 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
CCE
2006
13 years 9 months ago
State-of-the-art review of optimization methods for short-term scheduling of batch processes
There has been significant progress in the area of short-term scheduling of batch processes, including the solution of industrial-sized problems, in the last 20 years. The main go...
Carlos A. Méndez, Jaime Cerdá, Ignac...
WSC
2004
13 years 10 months ago
Exact Simulation of Option Greeks under Stochastic Volatility and Jump Diffusion Models
This paper derives Monte Carlo simulation estimators to compute option price derivatives, i.e., the `Greeks,' under Heston's stochastic volatility model and some variant...
Mark Broadie, Özgür Kaya
PAMI
2010
124views more  PAMI 2010»
13 years 3 months ago
Structural Approach for Building Reconstruction from a Single DSM
We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a li...
Florent Lafarge, Xavier Descombes, Josiane Zerubia...
ICASSP
2011
IEEE
13 years 17 days ago
A Bernoulli-Gaussian model for gene factor analysis
This paper investigates a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for gene factor analysis. Each sample in the dataset is decomposed as a linear combination...
Cecile Bazot, Nicolas Dobigeon, Jean-Yves Tournere...