Graph_sampler: a simple tool for fully Bayesian analyses of DAG-models

Sagnik Datta (), Ghislaine Gayraud (), Eric Leclerc () and Frederic Y. Bois
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Sagnik Datta: Sorbonne Universités, Université de Technologie de Compiègne
Ghislaine Gayraud: Sorbonne Universités, Université de Technologie de Compiègne
Eric Leclerc: The University of Tokyo
Frederic Y. Bois: INERIS DRC/VIVA/METO Parc ALATA

Computational Statistics, 2017, vol. 32, issue 2, No 14, 716 pages

Abstract: Abstract Bayesian networks (BNs) are widely used graphical models usable to draw statistical inference about directed acyclic graphs. We presented here Graph_sampler a fast free C language software for structural inference on BNs. Graph_sampler uses a fully Bayesian approach in which the marginal likelihood of the data and prior information about the network structure are considered. This new software can handle both the continuous as well as discrete data and based on the data type two different models are formulated. The software also provides a wide variety of structure prior which can depict either the global or local properties of the graph structure. Now based on the type of structure prior selected, we considered a wide range of possible values for the prior making it either informative or uninformative. We proposed a new and much faster jumping kernel strategy in the Metropolis–Hastings algorithm. The source C code distributed is very compact, fast, uses low memory and disk storage. We performed out several analyses based on different simulated data sets and synthetic as well as real networks to discuss the performance of Graph_sampler.

Keywords: Bayesian networks; Structure learning; Posterior distribution; MCMC; Metropolis–Hasting algorithm (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-017-0719-1

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