Bayes Linear Calibrated Prediction for Complex Systems

Bayes Linear Calibrated Prediction for Complex Systems

A calibration-based approach is developed for predicting the behaviour of a physical system which is modelled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterisations which appear to give good matches to those observations. This approach can be applied to complex high-dimensional systems with e...

Complex fuzzy linear systems

complex fuzzy linear systems

Algebraic Solving of Complex Interval Linear Systems by Limiting ‎Factors‎

In this work, we propose a simple method for obtaining the algebraic solution of a complex interval linear system where coefficient matrix is a complex matrix and the right-hand-side vector is a complex interval vector. We first use a complex interval version of the Doolittle decomposition method and then we restrict the Doolittle's solution, by complex limiting factors, to achieve a complex in...

Simplifying Complex Designs: Bayes Linear Experimental Design for Grouped Multivariate Exchangeable Systems

We adopt a Bayes linear approach to tackle design problems with many variables cross-classified in many ways. We investigate designs where we wish to sample individuals belonging to different groups, exploiting the powerful properties of the adjustment of second-order exchangeable vectors. The types of information we gain by sampling are identified with the orthogonal canonical directions. We s...