Efficient estimation of expected information gain in Bayesian experimental design with multi-index Monte Carlo

Expected information gain (EIG) is an important criterion in Ba yesian optimal experimental design. Nested Monte Carlo and M ulti-level Monte Carlo (MLMC) methods have been used to compute EIG. However, in cases where the forward output function is not analytically tractable, even MLMC can not achie...

Full description

Saved in:
Bibliographic Details
Published in:Statistics and computing 2024-12, Vol.34 (6), Article 200
Main Authors: Du, Xinting, Wang, Hejin
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Design of experiments
Original Paper
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Expected information gain (EIG) is an important criterion in Ba yesian optimal experimental design. Nested Monte Carlo and M ulti-level Monte Carlo (MLMC) methods have been used to compute EIG. However, in cases where the forward output function is not analytically tractable, even MLMC can not achieve its best rate. In this paper, we use Multi-index Monte Carlo to compute the EIG, which can give O ( ε - 2 ) computation work. Both theoretical analysis and numerical results are presented.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-024-10522-5