ArticleProceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)2020
Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points.
Article in Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Data driven drift correction for complex optical systems.Journal of synchrotron radiation · 2026Article
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2 authors.
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Abstract
We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.