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 | Archive PDF | Plain Text | 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. |