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Markov Chain Monte Carlo model selection for DAG models

Author

Listed:
  • Eva-Maria Fronk

    (Ludwig-Maximilians-University)

  • Paolo Giudici

    (University of Pavia)

Abstract

. We present a methodology for Bayesian model choice and averaging in Gaussian directed acyclic graphs (dags). The dimension-changing move involves adding or dropping a (directed) edge from the graph. The methodology employs the results in Geiger and Heckerman and searches directly in the space of all dags. Model determination is carried out by implementing a reversible jump Markov Chain Monte Carlo sampler. To achieve this aim we rely on the concept of adjacency matrices, which provides a relatively inexpensive check for acyclicity. The performance of our procedure is illustrated by means of two simulated datasets, as well as one real dataset.

Suggested Citation

  • Eva-Maria Fronk & Paolo Giudici, 2004. "Markov Chain Monte Carlo model selection for DAG models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 13(3), pages 259-273, December.
    • Handle: RePEc:spr:stmapp:v:13:y:2004:i:3:d:10.1007_s10260-004-0097-z
    DOI: 10.1007/s10260-004-0097-z
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    Citations

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    Cited by:

    1. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2012. "Bayesian Graphical Models for Structural Vector Autoregressive Processes," Working Papers 2012:36, Department of Economics, University of Venice "Ca' Foscari".
    2. Elie Bouri & Rangan Gupta & Seyedmehdi Hosseini & Chi Keung Marco Lau, 2017. "Does Global Fear Predict Fear in BRICS Stock Markets? Evidence from a Bayesian Graphical VAR Model," Working Papers 201704, University of Pretoria, Department of Economics.
    3. Webb, Emily L. & Forster, Jonathan J., 2008. "Bayesian model determination for multivariate ordinal and binary data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2632-2649, January.
    4. Helen Armstrong & Christopher K. Carter & Kevin K. F. Wong & Robert Kohn, 2007. "Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models," Discussion Papers 2007-13, School of Economics, The University of New South Wales.
    5. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    6. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    7. repec:jss:jstsof:14:i11 is not listed on IDEAS
    8. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.

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