A Complete Characterization of Projectivity for Statistical Relational Models
Authors: Manfred Jaeger, Oliver Schulte
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we fill this gap: exploiting representation theorems for infinite exchangeable arrays we introduce a class of directed graphical latent variable models that precisely correspond to the class of projective relational models. As a by-product we also obtain a characterization for when a given distribution over size-k structures is the statistical frequency distribution of size-k substructures in much larger size-n structures. These results shed new light onto the old open problem of how to apply Halpern et al. s random worlds approach for probabilistic inference to general relational signatures. |
| Researcher Affiliation | Academia | Manfred Jaeger1 and Oliver Schulte2 1Computer Science Department, Aalborg University, Aalborg, Denmark 2School of Computing Science, Simon Fraser University, Burnaby, Canada |
| Pseudocode | No | The paper contains definitions, propositions, and theorems, but no pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper mentions an extended online version for the full proof, but does not provide any link or statement indicating the release of source code for the models or methods described in the paper. It refers to third-party systems or resources, but not its own implementation code. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available or open datasets for empirical evaluation. Examples used are for theoretical illustration. |
| Dataset Splits | No | The paper is theoretical and does not include empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental setups, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for reproducing empirical work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |