Joining and splitting models with Markov melding
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2016
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| _version_ | 1866914400174080000 |
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| author | Goudie, Robert J. B. Presanis, Anne M. Lunn, David De Angelis, Daniela Wernisch, Lorenz |
| author_facet | Goudie, Robert J. B. Presanis, Anne M. Lunn, David De Angelis, Daniela Wernisch, Lorenz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1607_06779 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | Joining and splitting models with Markov melding Goudie, Robert J. B. Presanis, Anne M. Lunn, David De Angelis, Daniela Wernisch, Lorenz Methodology Applications Computation 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. |
| title | Joining and splitting models with Markov melding |
| topic | Methodology Applications Computation |
| url | https://arxiv.org/abs/1607.06779 |