A Unifying Framework for Probabilistic Belief Revision
Authors: Zhiqiang Zhuang, James Delgrande, Abhaya Nayak, Abdul Sattar
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a representation theorem for p-revision which shows that it can be characterised by the set of basic AGM revision postulates. P-revision represents an all purpose method for revising probabilistic information that can be used for, but not limited to, the revision problems behind Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis s imaging. Importantly, p-revision subsumes the above three approaches indicating that Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis imaging all obey the basic principles of AGM revision. |
| Researcher Affiliation | Academia | Zhiqiang Zhuang Griffith University z.zhuang@griffith.edu.au James Delgrande Simon Fraser University jim@cs.sfu.ca Abhaya Nayak Macquarie University abhaya.nayak@mq.edu.au Abdul Sattar Griffith University a.sattar@griffith.edu.au |
| Pseudocode | No | The paper contains mathematical definitions and theorems but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the described methodology. No links or statements about code availability are present. |
| Open Datasets | No | The paper is purely theoretical and does not involve empirical studies or datasets, so there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies, datasets, or any form of training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware specifications used for computation. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training settings, as it does not describe empirical experiments. |