Optimal Learning in Experimental Design Using the Knowledge Gradient Policy with Application to Characterizing Nanoemulsion Stability

DOI10.1137/140971129zbMath1327.62098OpenAlexW2076295623MaRDI QIDQ2945155

Maneesh K. Gupta, Kristofer-Roy G. Reyes, Warren B. Powell, Si Chen, Michael C. McAlpine

Publication date: 9 September 2015

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/8b629ff3e816d070aabb5d416a88ba23f16f372d

zbMATH Keywords

Bayesian analysismaterials sciencesequential decision makingoptimal learningknowledge gradientnanoemulsion

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) Stochastic learning and adaptive control (93E35) Sequential statistical design (62L05) Statistical ranking and selection procedures (62F07) Discrete approximations in optimal control (49M25)

Related Items (7)

Optimal Learning for Nonlinear Parametric Belief Models Over Multidimensional Continuous Spaces ⋮ Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles ⋮ Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models ⋮ Uncertainty propagation of p-boxes using sparse polynomial chaos expansions ⋮ A unified framework for stochastic optimization ⋮ Optimal Online Learning for Nonlinear Belief Models Using Discrete Priors ⋮ Optimal Learning with Local Nonlinear Parametric Models over Continuous Designs

Uses Software

• BayesDA
• ElemStatLearn

Cites Work

• Macroscopic limits of the Becker-Döring equations
• The Becker-Döring equations at large times and their connection with the LSW theory of coarsening
• Proof of dynamical scaling in Smoluchowski's coagulation equation with constant kernel
• The Knowledge-Gradient Policy for Correlated Normal Beliefs
• The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
• Approximate Dynamic Programming
• A Knowledge-Gradient Policy for Sequential Information Collection
• Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures
• Methods and Criteria for Model Selection
• The elements of statistical learning. Data mining, inference, and prediction
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