In this paper, we argue that some of the prior parameter distributions used in the literature for the construction of Bayesian optimal designs are internally inconsistent. We provide practical advice on how to properly specify the prior parameter distribution. Also, we present an example to illustrate that Bayesian optimal designs generally outperform utility‐neutral optimal designs that are based on linear design principles. Copyright (c) 2008 John Wiley & Sons, Ltd
This thesis progresses Bayesian experimental design by developing novel methodologies and extensions...
The emphasis in this work is on derivation of optimal Bayes inferences and designs in relatively une...
In industrial experiments, cost considerations will sometimes make it impractical to design experime...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Bayesian design theory applied to nonlinear models is a promising route to cope with the problem of ...
Bayesian experimental design is a fast growing area of research with many real-world applications. A...
This thesis progresses Bayesian experimental design by developing novel methodologies and extensions...
The emphasis in this work is on derivation of optimal Bayes inferences and designs in relatively une...
In industrial experiments, cost considerations will sometimes make it impractical to design experime...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
In this paper, we argue that some of the prior parameter distributions used in the literature for th...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated ch...
Bayesian design theory applied to nonlinear models is a promising route to cope with the problem of ...
Bayesian experimental design is a fast growing area of research with many real-world applications. A...
This thesis progresses Bayesian experimental design by developing novel methodologies and extensions...
The emphasis in this work is on derivation of optimal Bayes inferences and designs in relatively une...
In industrial experiments, cost considerations will sometimes make it impractical to design experime...