Averaged Probabilistic Relational Models

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Abstract/Contents

Abstract: Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of Probabilistic Relational Models (PRMs) allows us to represent probabilistic models over multiple entities that utilize the relations between them. However, for extremely large domains it may be impossible to represent every object and every relation in the domain explicitly. We propose representing the domain as an Averaged PRM using only “schema level” statistical information about the objects and relations, and present an approximation algorithm for reasoning about the domain with only this information. We present experimental results showing that interesting inferences can be made about extremely large domains, with a running time that does not depend on the number of objects.

Type of resource	text
Date created	2002-06-03

Author	Wright, Daniel
Advisor	Koller, Daphne
Department	Stanford University. Department of Computer Science.

Subject	Bayesian statistical decision theory > Data processing
Subject	Modeling
Subject	Firestone Medal for Excellence in Undergraduate Research
Subject	Co-winner Ben Wegbreit Prize for Best Undergraduate Honors Thesis in Computer Science
Genre	Thesis

Location	https://purl.stanford.edu/qm695ny4920

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License: This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

Preferred Citation: Wright, Daniel (2002). Averaged Probabilistic Relational Models. Stanford Digital Repository. Available at http://purl.stanford.edu/qm695ny4920

Undergraduate Theses, School of Engineering

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