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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums
Authors: P.-L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give detailed examples demonstrating our results. This result is again easily verified through direct inversion of J. In addition, we have consistently observed in numerical experiments that the contribution of a simple cycle/path to any path-sum decays exponentially with its length. |
| Researcher Affiliation | Academia | Department of Computer Science University of York, Department of Statistics University of Oxford, Department of Physics and Arnold Sommerfeld Center for Theoretical Physics, Ludwig-Maximilians-Universität at München, Department of Physics University of Oxford. |
| Pseudocode | No | The paper describes mathematical formulations and provides examples of their application but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code, nor does it provide links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper uses mathematical examples, such as a 'circle graph on 5 vertices, denoted C5' and a 'thin membrane model', rather than experimental data from a publicly available dataset. No datasets with concrete access information are provided. |
| Dataset Splits | No | The paper does not utilize external datasets for experimentation and therefore does not provide any information regarding dataset splits. |
| Hardware Specification | No | The paper mentions 'numerical experiments' but does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to conduct these experiments. |
| Software Dependencies | No | The paper does not specify any ancillary software dependencies or their version numbers that would be required to replicate the work. |
| Experiment Setup | No | The paper focuses on mathematical formulations and provides illustrative examples with specific parameter values for the mathematical models (e.g., 'r = 0.3' or 'a = b = 1'), but it does not describe an experimental setup with hyperparameters or training configurations typically found in empirical studies. |