Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes

Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter and prediction uncertainties. Yet, generating representative samples from the posterior distribution is often computationally challenging. Here, we present an ap...

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Detalles Bibliográficos
Autores principales: Raimúndez, Elba, Fedders, Michael, Hasenauer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589897/
https://www.ncbi.nlm.nih.gov/pubmed/37867942
http://dx.doi.org/10.1016/j.isci.2023.108083

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589897/
https://www.ncbi.nlm.nih.gov/pubmed/37867942
http://dx.doi.org/10.1016/j.isci.2023.108083

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