Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Coherent Predictive Inference under Exchangeability with Imprecise Probabilities
Authors: Gert De Cooman, Jasper De Bock, Márcio Alves Diniz
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper deals with predictive inference for categorical variables... Its aim is to study and develop a general framework for dealing with conservative coherent predictive inference using imprecise probability models... We formulate these principles as properties of so-called coherent inference systems, and study their mathematical implications for conservative predictive inference. Also, the paper contains numerous theorems, propositions, and proofs, with a dedicated Appendix E for 'Proofs and Additional Results That Are More Technical'. |
| Researcher Affiliation | Academia | Gert de Cooman EMAIL Jasper De Bock EMAIL Ghent University, SYSTe MS Research Group Technologiepark Zwijnaarde 914 9052 Zwijnaarde, Belgium. Márcio Alves Diniz EMAIL Federal University of São Carlos, Department of Statistics Rod. Washington Luis, km 235 São Carlos, Brazil. |
| Pseudocode | No | The paper contains mathematical definitions, theorems, propositions, and proofs. There are no explicitly labeled pseudocode or algorithm blocks, nor are there any structured steps formatted like code. |
| Open Source Code | No | The paper is theoretical and focuses on developing a mathematical framework. There is no statement or link indicating the release of open-source code for the described methodology in the main text or appendices. |
| Open Datasets | No | The paper uses illustrative 'running examples' such as coin flips and a bag of marbles to explain theoretical concepts. It does not mention or provide access information for any publicly available or open datasets for experimental validation. |
| Dataset Splits | No | The paper does not conduct experiments on datasets; therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and describes a mathematical framework; it does not detail any experimental setup that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations. Consequently, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper focuses on theoretical development and mathematical proofs. It does not contain details about experimental setup, hyperparameters, model initialization, or training schedules. |