Uncertainty in Artificial Intelligence
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YGGDRASIL - A Statistical Package for Learning Split Models
Soren Hojsgaard
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 independences holding only for sepcific values of the conditioning variables. This framework is constituted by the class of split models. Split models are extension of graphical models for contigency tables and allow for a more sophisticiated 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.
Keywords:
Pages: 274-281
PS Link:
PDF Link: /papers/00/p274-hojsgaard.pdf
BibTex:
@INPROCEEDINGS{Hojsgaard00,
AUTHOR = "Soren Hojsgaard ",
TITLE = "YGGDRASIL - A Statistical Package for Learning Split Models",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "274--281"
}


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