Gradient-based stochastic optimization methods in bayesian experimental design

Gradient-based stochastic optimization methods in bayesian experimental design

En este artc̕ulo se desarrollan mťodos de optimizacin̤ estocs̀tica basados en gradientes para el diseǫ de experimentos sobre un espacio de parm̀etros continuo. Dado un estimador de Monte Carlo de ganancia de informacin̤ esperada, se utiliza un anl̀isis de perturbacin̤ infinitesimal para obtener lo...

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Detaylı Bibliyografya
Diğer Yazarlar: Huan Xun, Marzouk Youssef M., Cornell University
Materyal Türü: Kitap
Dil:İngilizce
Konular:
Administracin̤
Investigacin̤ operacional
TeorƯ̕a bayesiana de decisiones estads̕ticas
Management
Operations research
Bayesian statistical decision theory
Ciencias de la administracin̤
Optimizacin̤ estocs̀tica
Programacin̤ estocs̀tica
Mťodos estocs̀ticos basados en gradientes
Diseǫs experimentales bayesianos
Aproximacin̤ estocs̀tica robbins-monro
Aproximacin̤ promedio de muestras
Diseǫ p̤timo
Anl̀isis de perturbacin̤ infinitesimal
Inversin̤ de fuente
Administration sciences
Stochastic optimization
Stochastic programming
Stochastic methods based on gradients
Bayesian experimental designs
Robbins-monro stochastic approximation
Average approximation of samples
Optimal design
Infinitesimal perturbation analysis
Source inversion
Online Erişim:Gradient-based stochastic optimization methods in bayesian experimental design
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Gradient-based stochastic optimization methods in bayesian experimental design

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