Bayesian reliability optimization for multivariate binary responses

Article type: Research Article

Authors: Chakraborty, Sayan^* | Scott, Danny

Affiliations: Tibco Software Inc.

Correspondence: [*] Corresponding author: Sayan Chakraborty, 2300 E 14th St, Tulsa, OK 74104, USA. Tel.: +1 517 721 9686; E-mail: [email protected].

Abstract: Response Surface Methodology is a popular set of statistical techniques used to improve a system process. Peterson (2004) proposed a Bayesian multivariate response optimization method that considers the dependence structure among the responses when locating the optimal region as defined by some loss or desirability function. The main contribution of this paper lies in addressing the Bayesian reliability optimization for multivariate binary responses where the logistic models with traditional Bayesian reliability approach suffers from computational complexities. This work is focused on reducing the computational complexities by introducing latent variables in the response structure.

Keywords: Bayesian reliability, binary response, gibbs steps, latent variable, MCMC, response surface method

DOI: 10.3233/MAS-180432

Journal: Model Assisted Statistics and Applications, vol. 13, no. 3, pp. 297-308, 2018