Bayesian validation assessment of multivariate computational modelsXiaomo Jiang and Sankaran Mahadevan Journal of Applied Statistics, 2008, vol. 35, issue 1, pages 49-65 Abstract: Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes' theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty. Keywords: Bayesian statistics; decision making; risk; reliability; model validation; multivariate statistics (search for similar items in EconPapers) Downloads: (external link) Related works: Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Applied Statistics is edited by Professor Gopal K. Kanji More articles in Journal of Applied Statistics from Taylor and Francis Journals |
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