University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Searching parameter spaces by mapping likelihood

Searching parameter spaces by mapping likelihood

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The efficient use of negative evidence in search problems has always been important: for example, string search algorithms such as Boyer-Moore make use of negative evidence to achieve greatly increased speed. However, it is not always clear how negative evidence can be exploited in a probabilistic framework. In this talk, I explore the accumulation of negative and positive statistical evidence by building a map of likelihood in parameter space, allowing a directed search of this space. I illustrate the approach with simple line detection and image matching examples, which emphasise the value of accurately modelling (or learning) image statistics. I discuss the conditions under which the method may be useful, and I propose that the correct framework for it is not a Bayesian one, but rather the likelihood method of A.W.F. Edwards.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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