Combining Bayesian Networks with Higher-Order Data Representations

Elias Gyftodimos; Peter A. Flach

Combining Bayesian Networks with Higher-Order Data Representations

Elias Gyftodimos
, Peter A. Flach

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

5 Citations (Scopus)

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

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Conference Proceedings/Title of Journal: Probabilistic, Logical and Relationial Learning - Towards a Synthesis
Other identifier: 2000554

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@inproceedings{f54b741ec0a745d2ad5636ecfc4ad69b,

title = "Combining Bayesian Networks with Higher-Order Data Representations",

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. ",

author = "Elias Gyftodimos and Flach, \{Peter A.\}",

note = "Other page information: - Conference Proceedings/Title of Journal: Probabilistic, Logical and Relationial Learning - Towards a Synthesis Other identifier: 2000554",

year = "2005",

language = "English",

booktitle = "Probabilistic, Logical and Relationial Learning - Towards a Synthesis",

publisher = "Dagstuhl Seminar Proceedings 05051:",

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AU - Flach, Peter A.

N1 - Other page information: - Conference Proceedings/Title of Journal: Probabilistic, Logical and Relationial Learning - Towards a Synthesis Other identifier: 2000554

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

M3 - Conference Contribution (Conference Proceeding)

BT - Probabilistic, Logical and Relationial Learning - Towards a Synthesis

PB - Dagstuhl Seminar Proceedings 05051:

ER -

Combining Bayesian Networks with Higher-Order Data Representations

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