Bayesian Analysis

Adapting the ABC Distance Function

Dennis Prangle

Full-text: Open access

Abstract

Approximate Bayesian computation performs approximate inference for models where likelihood computations are expensive or impossible. Instead simulations from the model are performed for various parameter values and accepted if they are close enough to the observations. There has been much progress on deciding which summary statistics of the data should be used to judge closeness, but less work on how to weight them. Typically weights are chosen at the start of the algorithm which normalise the summary statistics to vary on similar scales. However these may not be appropriate in iterative ABC algorithms, where the distribution from which the parameters are proposed is updated. This can substantially alter the resulting distribution of summary statistics, so that different weights are needed for normalisation. This paper presents two iterative ABC algorithms which adaptively update their weights and demonstrates improved results on test applications.

Article information

Source
Bayesian Anal. Volume 12, Number 1 (2017), 289-309.

Dates
First available in Project Euclid: 14 April 2016

Permanent link to this document
https://projecteuclid.org/euclid.ba/1460641065

Digital Object Identifier
doi:10.1214/16-BA1002

Keywords
likelihood-free inference population Monte Carlo quantile distributions Lotka–Volterra

Rights
Creative Commons Attribution 4.0 International License.

Citation

Prangle, Dennis. Adapting the ABC Distance Function. Bayesian Anal. 12 (2017), no. 1, 289--309. doi:10.1214/16-BA1002. https://projecteuclid.org/euclid.ba/1460641065.


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