YGGDRASIL: a statistical package for learning split models

Authors:
Søren Højsgaard
Affiliations:
Biometry Research Unit, Danish Institute of Agricultural Sciences, Denmark
Venue:
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Year:
2000

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Abstract

There are two main objectives of this paper. The first is to present a statistical framework for models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables. This framework is constituted by the class of split models. Split models are an extension of graphical models for contingency tables and allow for a more sophisticated modelling than graphical models. The treatment of split models include estimation, representation and a Markov property for reading off those independencies holding in a specific context. The second objective is to present a software package named YGGDRASIL which is designed for statistical inference in split models, i.e. for learning such models on the basis of data.