Stochastic optimization algorithms of a Bayesian design criterion for Bayesian parameter estimation of nonlinear regression models: Application in pharmacokinetics
| Autor: | Yann Merlé, France Mentré |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Statistics and Probability
Stochastic Processes Bayes estimator Mathematical optimization General Immunology and Microbiology Applied Mathematics Bayes Theorem General Medicine Models Theoretical Stochastic approximation General Biochemistry Genetics and Molecular Biology Variable-order Bayesian network Estimation of distribution algorithm Bayesian information criterion Modeling and Simulation Prior probability Regression Analysis Pharmacokinetics Stochastic optimization General Agricultural and Biological Sciences Bayesian linear regression Algorithm Algorithms Mathematics |
| Zdroj: | Mathematical Biosciences. 144:45-70 |
| ISSN: | 0025-5564 |
| DOI: | 10.1016/s0025-5564(97)00017-5 |
| Popis: | This article proposes three stochastic algorithms to optimize a Bayesian design criterion for Bayesian estimation of the parameters of nonlinear regression models; this criterion is the information expected from an experiment. The first algorithm is based on a stochastic version of the simplex with an adaptive sampling procedure. The others are stochastic approximation algorithms: the Kiefer-Wolfowitz and the pseudogradient algorithms. We first present the information criterion and the optimization algorithms. The efficiency of each algorithm for optimizing this Bayesian design criterion is then assessed by a simulation study for a nonlinear model assuming a discrete prior distribution. An application for designing an experiment to estimate the kinetics of radioiodine thyroid uptake is then proposed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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