Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Version 1 : Received: 26 January 2022 / Approved: 27 January 2022 / Online: 27 January 2022 (04:55:25 CET)

A peer-reviewed article of this Preprint also exists.

Lee, S.Y. Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications. Mathematics 2022, 10, 898. Lee, S.Y. Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications. Mathematics 2022, 10, 898.

Journal reference: Mathematics 2022, 10, 898
DOI: 10.3390/math10060898

Abstract

Bayesian nonlinear mixed effects models for data in the form of continuous, repeated measurements from a population, also known as Bayesian hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual specific characteristics and relevant uncertainty quantification. Due to the limitation of computational power, this framework was relatively dormant until the late 1980s, but in recent years, the statistical research community saw vigorous development of new methodological and computational techniques for these models, the emergence of software, and wide application of the models in numerous industrial and academic fields. This article presents an overview of the formulation, interpretation, and implementation of Bayesian nonlinear mixed effects models and surveys recent advances and applications.

Keywords

Bayesian nonlinear hierarchical model; Bayesian nonlinear mixed effects models; Inter-individual variation; Intra-individual variation; Markov chain Monte Carlo technique

Subject

MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics

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