*Ioannis Tsamardinos, Alexander Statnikov, Laura E. Brown, Constantin F. Aliferis*

In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the structural and probabilistic properties of the tiles so that the distribution of the resulting tiled network resembles the real-world distribution of the original tiles. By generating networks of various sizes one can study the behavior of Bayesian Network learning algorithms as a function of the size of the networks only while the underlying probability distributions remain similar. We demonstrate through empirical evaluation examples how the networks produced by the algorithm enable researchers to conduct comparative evaluations of learning algorithms on large real-world Bayesian networks.

*Subjects:* 12. Machine Learning and Discovery; 12.2 Scientific Discovery

*Submitted:* Feb 10, 2006

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