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...

Description complète

Enregistré dans:
Détails bibliographiques
Autres auteurs: Huan Xun, Marzouk Youssef M., Cornell University
Format: Livre
Langue:anglais
Sujets:
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
Accès en ligne:Gradient-based stochastic optimization methods in bayesian experimental design
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!

Internet

Gradient-based stochastic optimization methods in bayesian experimental design

Documents similaires