Lifting Factor Graphs with Some Unknown Factors

Fuente: arXiv
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Autori principali: Luttermann, Malte, Möller, Ralf, Gehrke, Marcel
Natura: Preprint
Pubblicazione: 2024
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author Luttermann, Malte
Möller, Ralf
Gehrke, Marcel
author_facet Luttermann, Malte
Möller, Ralf
Gehrke, Marcel
contents Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing factors whose potentials are unknown. We introduce the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify symmetric subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics and allow for (lifted) probabilistic inference.
format Preprint
id arxiv_https___arxiv_org_abs_2406_01275
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Lifting Factor Graphs with Some Unknown Factors
Luttermann, Malte
Möller, Ralf
Gehrke, Marcel
Artificial Intelligence
Machine Learning
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing factors whose potentials are unknown. We introduce the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify symmetric subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics and allow for (lifted) probabilistic inference.
title Lifting Factor Graphs with Some Unknown Factors
topic Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2406.01275