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A Novel Gibbs Maximum A Posteriori (GMAP) Approach on Bayesian Nonlinear Mixed-Effects Population Pharmacokinetics (PK) Models
In this paper, various Bayesian Monte Carlo Markov Chain (MCMC) methods and the proposed algorithm, Gibbs maximum a posteriori (GMAP) algorithm, are compared for implementing the nonlinear mixed-effects model in pharmacokinetics (PK) studies. An intravenous two-compartmental PK model is adopted to f...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2009
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2829744/ https://ncbi.nlm.nih.gov/pubmed/20183435 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/10543400902964159 |
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