Bayesian Nonparametric Spectral Estimation
Authors: Felipe Tobar
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 ftobar@dim.uchile.cl |
| 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. |