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Joining and splitting models with Markov melding.


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.

SUBMITTER: Goudie RJB 

PROVIDER: S-EPMC6324725 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Joining and splitting models with Markov melding.

Goudie Robert J B RJB   Presanis Anne M AM   Lunn David D   De Angelis Daniela D   Wernisch Lorenz L  

Bayesian analysis 20190101 1


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 <i>joining</i> submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. The approach also enabl  ...[more]

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