Joining and splitting models with Markov melding

TL;DR

This paper introduces a Bayesian framework for joining and splitting submodels, enabling modular analysis of complex evidence sources with efficient multi-stage computation.

Contribution

It presents a novel, generic method for constructing joint models from submodels and a staged algorithm for efficient inference, facilitating modular analysis.

Findings

Effective joint modeling of evidence sources demonstrated

Multi-stage algorithm improves computational efficiency

Applicable to diverse fields like epidemiology and ecology

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.