An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Longitudinal data are common in biomedical research, but their analysis is often challenging. Here, the authors present an additive Gaussian process regression model specifically designed for statistical analysis of longitudinal experimental data.

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Autores principales: Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
Materias:
Science
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Acceso en línea:https://doaj.org/article/3e09ccc1e58a490b9b60b5290758f8fa
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