A comparison of one-stage and two-stage Bayesian processes in engineering decision-making

Abstract

Engineering decision-making problems often involve conditions that vary spatially either in a continuous way or from element to element. In such cases, the use of two-stage Bayesian models is often advisable in design optimization, maintenance and inspection planning. Two-stage models are based on a ‘state’ or ‘condition’ variable vector that is assumed to be conditionally independent with a pdf given a set of ‘hyper-parameters’. All other process parameters and responses are conditional on this state variable vector. Such models can be applied to a large variety of problems where data from various systems or sources need to be spatially ‘mixed’, such as in deteriorating infrastructure, spatial aspects of corrosion, preference and consequence modeling, and system failure models for large industrial plants. The models are described in detail and an extension is given that allows for the spatial correlation of the individual ‘states’ given the hyper-parameters.

Keywords:

• Bayesian inference
• Spatially distributed systems
• Engineering decision-making

Acknowledgements

The first author is grateful for funding from the NSERC discovery grant program (Canada) and from ConocoPhillips.