Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Authors: Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our method is very flexible and performs well compared to Hogwild-type algorithms. |
| Researcher Affiliation | Academia | Andrei-Cristian B arbos IMS Laboratory Univ. Bordeaux CNRS BINP andbarbos@u-bordeaux.fr François Caron Department of Statistics University of Oxford caron@stats.ox.ac.uk Jean-François Giovannelli IMS Laboratory Univ. Bordeaux CNRS BINP giova@ims-bordeaux.fr Arnaud Doucet Department of Statistics University of Oxford doucet@stats.ox.ac.uk |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes an application to image inpainting-deconvolution using an 'unobserved image... of size 1000x1000', but does not provide access information (link, DOI, specific citation) for a publicly available dataset. |
| Dataset Splits | No | The paper discusses 'burn-in samples' for MCMC, but does not specify dataset splits (e.g., percentages or counts for training, validation, or test sets). |
| Hardware Specification | Yes | Experiments are run on GPU with 2688 CUDA cores. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The tuning parameter η is set to 1. We run our clone MCMC algorithm for ns = 19000 samples, out of which the first 4000 were discarded as burn-in samples, using as initialization the observed image, with missing entries padded with zero. The observation noise is assumed to be independent of X with Σ 1 b = γb I and γb = 10 2. |