Sequential Sampling to Myopically Maximize the Expected Value of Information
From MaRDI portal
Publication:2899037
DOI10.1287/ijoc.1090.0327zbMath1243.62005OpenAlexW1986412858MaRDI QIDQ2899037
Christian Schmidt, Juergen Branke, Stephen E. Chick
Publication date: 28 July 2012
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.1090.0327
Bayesian problems; characterization of Bayes procedures (62C10) Sampling theory, sample surveys (62D05) Sequential statistical methods (62L99)
Related Items (23)
Top-\(\kappa\) selection with pairwise comparisons ⋮ Solving Large-Scale Fixed-Budget Ranking and Selection Problems ⋮ Dynamic Sampling Allocation and Design Selection ⋮ Optimal Learning for Nonlinear Parametric Belief Models Over Multidimensional Continuous Spaces ⋮ Sequential Selection with Unknown Correlation Structures ⋮ A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection ⋮ Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination ⋮ Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures ⋮ Ranking and selection for pairwise comparison ⋮ Sequential selection for accelerated life testing via approximate Bayesian inference ⋮ Information theory for ranking and selection ⋮ Asymptotic optimality of myopic ranking and selection procedures ⋮ Convergence rate analysis for optimal computing budget allocation algorithms ⋮ On the Convergence Rates of Expected Improvement Methods ⋮ Optimal Learning in Linear Regression with Combinatorial Feature Selection ⋮ Computational Efficiency in Multivariate Adversarial Risk Analysis Models ⋮ A unified framework for stochastic optimization ⋮ Simple Bayesian Algorithms for Best-Arm Identification ⋮ Optimal Learning with Local Nonlinear Parametric Models over Continuous Designs ⋮ Complete expected improvement converges to an optimal budget allocation ⋮ The Local Time Method for Targeting and Selection ⋮ Efficient Sampling Allocation Procedures for Optimal Quantile Selection ⋮ Optimal budget allocation policy for tabu search in stochastic simulation optimization
This page was built for publication: Sequential Sampling to Myopically Maximize the Expected Value of Information
