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 |
|---|---|
| Original language | English |
| Title of host publication | Probabilistic, Logical and Relationial Learning - Towards a Synthesis |
| Publisher | Dagstuhl Seminar Proceedings 05051: |
| Publication status | Published - 2005 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Probabilistic, Logical and Relationial Learning - Towards a Synthesis
Other identifier: 2000554