Abstract
This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of
Bayesian Networks with the expressive power of higher-order logics. We discuss
how the proposed graphical model is used in order to define a probability distribution semantics over particular families of higher-order terms. We give an example
of the application of our method on the Mutagenesis domain, a popular dataset
from the Inductive Logic Programming community, showing how we employ
probabilistic inference and model learning for the construction of a probabilistic
classifier based on Higher-Order Bayesian Networks.
Translated title of the contribution | Combining Bayesian Networks with Higher-Order Data Representations |
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Original language | English |
Title of host publication | Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06) |
Pages | 145-157 |
Publication status | Published - 2005 |
Bibliographical note
ISBN: 3540287957Publisher: Springer-Verlag
Name and Venue of Conference: Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)
Other identifier: 2000553