@inproceedings{2000553,
author={Elias Gyftodimos and Peter A. Flach},
title={Combining Bayesian Networks with Higher-Order Data Representations},
booktitle={Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)},
ISBN={3-540-28795-7},
publisher={Springer-Verlag},
pages={145--157},
month={September},
year={2005},
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. },
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000553},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000553.pdf},
keyword={Machine Learning},
pubtype={102}
}