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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...
| Formato: | Online Artículo Texto |
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| Lenguaje: | English |
| Publicado: |
IEEE
2022
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| 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 |