Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials

Goal: To develop a computationally efficient and unbiased synthetic data generator for large-scale in silico clinical trials (CTs). Methods: We propose the BGMM-OCE, an extension of the conventional BGMM (Bayesian Gaussian Mixture Models) algorithm to provide unbiased estimations regarding the optim...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970043/
https://www.ncbi.nlm.nih.gov/pubmed/36860496
http://dx.doi.org/10.1109/OJEMB.2022.3181796

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970043/
https://www.ncbi.nlm.nih.gov/pubmed/36860496
http://dx.doi.org/10.1109/OJEMB.2022.3181796

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