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Joining and splitting models with Markov melding.

Robert J B Goudie1, Anne M Presanis1, David Lunn1

  • 1MRC Biostatistics Unit, University of Cambridge, United Kingdom.

Bayesian Analysis
|January 12, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a Bayesian framework for analyzing evidence using modular submodels. The method allows joining or splitting complex models for efficient, staged computation and inference.

Keywords:
Bayesian meldingMarkov combinationevidence synthesismodel integration

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Area of Science:

  • Statistical modeling
  • Computational statistics
  • Evidence synthesis

Background:

  • Analyzing multiple evidence sources often requires modular approaches with separate submodels.
  • A fully Bayesian analysis in such settings is challenging with monolithic models.

Purpose of the Study:

  • Introduce a generic Bayesian framework for modular evidence analysis.
  • Develop methods for joining and splitting statistical models.
  • Enable efficient, staged computation for complex models.

Main Methods:

  • Propose a generic method for forming joint models from submodels.
  • Develop a computational algorithm for fitting joint models in stages.
  • Demonstrate model splitting for large, complex models.

Main Results:

  • A generic framework for fully Bayesian analysis of modular evidence is introduced.
  • A method for joining submodels into a joint model is proposed.
  • A staged computational algorithm facilitates fitting and inference for large models.

Conclusions:

  • The proposed framework enables flexible and efficient Bayesian analysis of complex evidence structures.
  • The approach is applicable to both joining smaller models and splitting larger ones.
  • Demonstrated utility in evidence synthesis for A/H1N1 influenza and ecology modeling.