A practical method for discriminating among mechanistic models

Thomas C. Hsiang · Park M. Reilly

48 citations0/0 files storedThe Canadian Journal of Chemical Engineering

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Optimal Experimental Design Methods Control Systems and Identification Statistical Methods and Bayesian Inference

Abstract

Abstract A method is presented for designing and analysing experiments for choosing the best mechanistic model among a set of more than one to describe a real‐world relationship. The method is a personalistic Bayesian one which does not depend on linearization of the models or on any specific form of the error distributions. Examples illustrating different types of applications of the method are given

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1967Discrimination Among Mechanistic Models 1970Statistical methods in model discrimination 1970Sequential discrimination and estimation procedures for rate modeling in heterogeneous catalysis

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2009Optimal Experimental Design for Discriminating Numerous Model Candidates: The AWDC Criterion 2021Iterative model‐based experimental design for spherical agglomeration processes 1997Discriminating between the Terminal and Penultimate Models Using Designed Experiments: An Overview

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