Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

  • Grzegorczyk M
  • Husmeier D
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Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional {MCMC} schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in the space of node orders. A disadvantage of the latter approach is the intrinsic inability to specify the prior probability on network structures explicitly. The relative paucity of different experimental conditions in contemporary systems biology implies a strong influence of the prior probability on the posterior probability and, hence, the outcome of inference. Consequently, the paradigm of performing {MCMC} proposal moves in order rather than structure space is not entirely satisfactory. In the present article, we propose a new and more extensive edge reversal move in the original structure space, and we show that this significantly improves the convergence of the classical structure {MCMC} scheme.

Author-supplied keywords

  • Bayesian networks
  • MCMC sampling
  • Structure learning

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  • Marco Grzegorczyk

  • Dirk Husmeier

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