Context-dependent Ranking and Selection under a Bayesian Framework

12/10/2020
by   Haidong Li, et al.
0

We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampling scheme for context-dependent optimization (DSCO) to efficiently learn and select the best designs in all contexts. The proposed sampling scheme is proved to be consistent. Numerical experiments show that the proposed sampling scheme significantly improves the efficiency in context-dependent ranking and selection.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/10/2020

Efficient Learning for Clustering and Optimizing Context-Dependent Designs

We consider a simulation optimization problem for a context-dependent de...
research
06/12/2019

Knowledge Gradient for Selection with Covariates: Consistency and Computation

Knowledge gradient is a design principle for developing Bayesian sequent...
research
05/06/2023

Efficient Learning for Selecting Top-m Context-Dependent Designs

We consider a simulation optimization problem for a context-dependent de...
research
08/19/2016

Automatic Selection of Context Configurations for Improved Class-Specific Word Representations

This paper is concerned with identifying contexts useful for training wo...
research
08/03/2022

Bayesian ranking and selection with applications to field studies, economic mobility, and forecasting

Decision-making often involves ranking and selection. For example, to as...
research
05/30/2018

Context Exploitation using Hierarchical Bayesian Models

We consider the problem of how to improve automatic target recognition b...
research
10/07/2017

Ranking and Selection with Covariates for Personalized Decision Making

We consider a ranking and selection problem in the context of personaliz...

Please sign up or login with your details

Forgot password? Click here to reset