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Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points
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-va...
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| Опубликовано в: : | Proc Eur Signal Process Conf EUSIPCO |
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| Главные авторы: | , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
2020
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7890411/ https://ncbi.nlm.nih.gov/pubmed/33614428 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.23919/Eusipco47968.2020.9287481 |
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