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» On Using Monte Carlo Methods for Scheduling
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ML
2002
ACM
220views Machine Learning» more  ML 2002»
14 years 11 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
14 years 12 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
15 years 1 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»
14 years 6 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
14 years 3 months 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...