A Bayesian mixture-of-Gaussians model for astronomical observations in interferometry

Araya-Hernandez, Lerko; Silva, Jorge F.; Osses, Axel; Tobar, Felipe

doi:10.1109/chilecon.2017.8229662

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Monitoring the spectral content of a time series is of critical importance in real-world applications across a wide range of scientific disciplines. This is because the concentration of energy at a specific range of frequencies might be indicative of mechanical faults [1], cardiac anomalies [2], astronomical discoveries [3,4], and whale calls from submarine audio recordings [5] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Low-pass Filtering as Bayesian Inference

Valenzuela

Tobar

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a Bayesian nonparametric method for low-pass filtering that can naturally handle unevenly-sampled and noise-corrupted observations. The proposed model is constructed as a latent-factor model for time series, where the latent factors are Gaussian processes with non-overlapping spectra. With this construction, the low-pass version of the time series can be identified as the lowfrequency latent component, and therefore it can be found by means of Bayesian inference. We show that the model admits exact training and can be implemented with minimal numerical approximations. Finally, the proposed model is validated against standard linear filters on synthetic and real-world time series.

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