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http://dx.doi.org/10.5351/KJAS.2021.34.1.009

A variational Bayes method for pharmacokinetic model  

Parka, Sun (Department of Statistics, Jeonbuk National University)
Jo, Seongil (Department of Statistics, Inha University)
Lee, Woojoo (Graduate School of Public Health, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.1, 2021 , pp. 9-23 More about this Journal
Abstract
In the following paper we introduce a variational Bayes method that approximates posterior distributions with mean-field method. In particular, we introduce automatic differentiation variation inference (ADVI), which approximates joint posterior distributions using the product of Gaussian distributions after transforming parameters into real coordinate space, and then apply it to pharmacokinetic models that are models for the study of the time course of drug absorption, distribution, metabolism and excretion. We analyze real data sets using ADVI and compare the results with those based on Markov chain Monte Carlo. We implement the algorithms using Stan.
Keywords
automatic differentiation variational inference; markov chain monte carlo; pharmacokinetic models; stan; variational bayes;
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