Bayesian Analysis

Joining and Splitting Models with Markov Melding

Robert J. B. Goudie, Anne M. Presanis, David Lunn, Daniela De Angelis, and Lorenz Wernisch

Full-text: Open access

Abstract

Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when joining submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. The approach also enables splitting of large joint models into smaller submodels, allowing inference for the original joint model to be conducted via our multi-stage algorithm. We motivate and demonstrate our approach through two examples: joining components of an evidence synthesis of A/H1N1 influenza, and splitting a large ecology model.

Article information

Source
Bayesian Anal., Volume 14, Number 1 (2019), 81-109.

Dates
First available in Project Euclid: 14 April 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1523671251

Digital Object Identifier
doi:10.1214/18-BA1104

Mathematical Reviews number (MathSciNet)
MR3910039

Zentralblatt MATH identifier
07001976

Keywords
model integration Markov combination Bayesian melding evidence synthesis

Rights
Creative Commons Attribution 4.0 International License.

Citation

Goudie, Robert J. B.; Presanis, Anne M.; Lunn, David; De Angelis, Daniela; Wernisch, Lorenz. Joining and Splitting Models with Markov Melding. Bayesian Anal. 14 (2019), no. 1, 81--109. doi:10.1214/18-BA1104. https://projecteuclid.org/euclid.ba/1523671251


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