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..
Bayesian Nonparametric Spectral Estimation
Authors: Felipe Tobar
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparison with previous approaches, in particular against Lomb-Scargle, is addressed theoretically and also experimentally in three different scenarios. Code and demo available at github.com/GAMES-UChile. This experimental section contains three parts focusing respectively on: (i) consistency of BNSE in the classical sum-of-sinusoids setting, (ii) robustness of BNSE to overfit and ability to handle non-uniformly sampled noisy observations (heart-rate signal), and (iii) exploiting the functional form of the PSD estimate of BNSE to find periodicities (astronomical signal). |
| Researcher Affiliation | Academia | Felipe Tobar Universidad de Chile EMAIL |
| Pseudocode | No | No. The paper describes the proposed model and methods mathematically and textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and demo available at github.com/GAMES-UChile. |
| Open Datasets | Yes | We next considered two heart-rate signals from http://ecg.mit.edu/time-series/. Lastly, we considered the sunspots dataset, an astronomical time series that is known to have a period of approximately 11 years... |
| Dataset Splits | No | No. While the paper mentions using |
| Hardware Specification | No | No. The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. It mentions using 'GPflow' which implies computational resources, but no specifications are given. |
| Software Dependencies | No | No. The paper mentions using |
| Experiment Setup | Yes | The window parameter was set to α = 1/(2 502) for an observation neighbourhood much wider than the support of the observations, and we chose an SM kernel with rather permissive hyperparameters: a rate γ = 1/(2 0.052) and θ = 0 for a prior over frequencies virtually uninformative. We implemented BNSE with a lengthscale equal to one and θ = 0 for a broad prior over frequencies, and α = 10 3 for a wide observation neighbourhood. |