Lifting Factor Graphs with Some Unknown Factors
This work addresses a specific challenge in probabilistic graphical models for researchers in AI and machine learning, representing an incremental advancement in lifting techniques.
The paper tackles the problem of performing probabilistic inference in factor graphs with unknown factors by introducing the LIFAGU algorithm, which identifies symmetric subgraphs to transfer known potentials to unknown ones, enabling lifted inference while maintaining exact answers.
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.