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UBC Theses and Dissertations

A practical solution for linear inference computations Schlax, Michael Garfield


In this thesis, I shall present a matrix form of Backus' theory of linear inference with multiple predictions. The Bayesian approach of Backus allows the treatment of erroneous data and the imposition of the essential a priori bound on the model norm. The X² statistic will be introduced to construct a most likely model and bound the norm of all acceptable models from above and below. This results in more reliable, and possibly more confining, estimates of the predictions than provided by only an upper bound. The algorithm derived is robust and efficient, and estimates comparable to those obtained from Oldenburg's linear programming algorithm have been achieved.

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