Paper Info
Title
An induced natural selection heuristic for finding optimal Bayesian experimental designs.
Abstract
Bayesian optimal experimental design has immense potential to inform the collection of data so as to subsequently enhance our understanding of a variety of processes. However, a major impediment is the difficulty in evaluating optimal designs for problems with large, or high-dimensional, design spaces. An efficient search heuristic suitable for general optimisation problems, with a particular focus on optimal Bayesian experimental design problems, is proposed. The heuristic evaluates the objective (utility) function at an initial, randomly generated set of input values. At each generation of the algorithm, input values are “accepted” if their corresponding objective (utility) function satisfies some acceptance criteria, and new inputs are sampled about these accepted points. The new algorithm is demonstrated by evaluating the optimal Bayesian experimental designs for the previously considered death, pharmacokinetic and logistic regression models. Comparisons to the current “gold-standard” method are given to demonstrate the proposed algorithm as a computationally-efficient alternative for moderately-large design problems (i.e., up to approximately 40-dimensions).
Year
DOI
Venue
2018
10.1016/j.csda.2018.04.011
Computational Statistics & Data Analysis
Keywords
Field
DocType
Bayesian optimal design,Optimisation heuristic,Stochastic models,Sampling windows
Econometrics,Bayesian experimental design,Heuristic,Mathematical optimization,Natural selection,Optimal design,Acceptance testing,Statistics,Logistic regression,Mathematics,Bayesian probability,Design of experiments
Journal
Volume
ISSN
Citations 
126
0167-9473
2
PageRank 
References 
Authors
0.43
4
4
Name
Order
Citations
PageRank
David J. Price120.77
nigel g bean24710.77
Joshua V. Ross321.79
jonathan tuke442.49