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MCMC methods for inference in a mathematical model of pulmonary circulation

Author

Listed:
  • L. Mihaela Păun
  • M. Umar Qureshi
  • Mitchel Colebank
  • Nicholas A. Hill
  • Mette S. Olufsen
  • Mansoor A. Haider
  • Dirk Husmeier

Abstract

This study performs parameter inference in a partial differential equations system of pulmonary circulation. We use a fluid dynamics network model that takes selected parameter values and mimics the behaviour of the pulmonary haemodynamics under normal physiological and pathological conditions. This is of medical interest as it enables tracking the progression of pulmonary hypertension. We show how we make the fluids model tractable by reducing the parameter dimension from a 55D to a 5D problem. The Delayed Rejection Adaptive Metropolis algorithm, coupled with constraint non‐linear optimization, is successfully used to learn the parameter values and quantify the uncertainty in the parameter estimates. To accommodate for different magnitudes of the parameter values, we introduce an improved parameter scaling technique in the Delayed Rejection Adaptive Metropolis algorithm. Formal convergence diagnostics are employed to check for convergence of the Markov chains. Additionally, we perform model selection using different information criteria, including Watanabe Akaike Information Criteria.

Suggested Citation

  • L. Mihaela Păun & M. Umar Qureshi & Mitchel Colebank & Nicholas A. Hill & Mette S. Olufsen & Mansoor A. Haider & Dirk Husmeier, 2018. "MCMC methods for inference in a mathematical model of pulmonary circulation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 306-338, August.
  • Handle: RePEc:bla:stanee:v:72:y:2018:i:3:p:306-338
    DOI: 10.1111/stan.12132
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    References listed on IDEAS

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