Explicit probabilistic models for databases and networks

Bie Tijl De

Research output: Working paperWorking paper and Preprints


Recent work in data mining and related areas has highlighted the importance of the statistical assessment of data mining results. Crucial to this endeavour is the choice of a non-trivial null model for the data, to which the found patterns can be contrasted. The most influential null models proposed so far are defined in terms of invariants of the null distribution. Such null models can be used by computation intensive randomization approaches in estimating the statistical significance of data mining results. Here, we introduce a methodology to construct non-trivial probabilistic models based on the maximum entropy (MaxEnt) principle. We show how MaxEnt models allow for the natural incorporation of prior information. Furthermore, they satisfy a number of desirable properties of previously introduced randomization approaches. Lastly, they also have the benefit that they can be represented explicitly. We argue that our approach can be used for a variety of data types. However, for concreteness, we have chosen to demonstrate it in particular for databases and networks.
Translated title of the contributionExplicit probabilistic models for databases and networks
Original languageEnglish
PublisherUniversity of Bristol
Number of pages25
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Explicit probabilistic models for databases and networks'. Together they form a unique fingerprint.

Cite this