# Generative Inferences Based on a Discriminative Bayesian Model of Relation Learning

- Dawn Chen,
*Department of Psychology, UCLA*
- Hongjing Lu,
*Departments of Psychology and Statistics, UCLA*
- Keith Holyoak,
*Department of Psychology, UCLA*

## Abstract

Bayesian Analogy with Relational Transformations (BART) is a
discriminative model that can learn comparative relations from non-relational
inputs (Lu, Chen & Holyoak, 2012). Here we show that BART can be extended to
solve inference problems that require generation (rather than classification) of
relation instances. BART can use its generative capacity to perform hypothetical
reasoning, enabling it to make quasi-deductive transitive inferences (e.g.,
“If A is larger than B, and B is larger than C, is A larger than
C?”). The extended model can also generate human-like instantiations of a
learned relation (e.g., answering the question, “What is an animal that is
smaller than a dog?”). These modeling results suggest that discriminative
models, which take a primarily bottom-up approach to relation learning, are
potentially capable of using their learned representations to make generative
inferences.

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