The formation of robust communication networks between independently acting agents is of practical interest in multiple domains, for example, in sensor placement and Unmanned Aerial Vehicle communication. These are th...
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Digital elevation models (DEMs) represent the Earth’s topography and support a variety of applications, ranging from extracting watershed drainage structure to measuring glacier volume. Applications that require conf...
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The nelder–mead or simplex search algorithm is one of the best known algorithms for unconstrained optimization of non–smooth functions. Even though the basic algorithm is quite simple, it is implemented in many diff...
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A traditional way to design a binary response experiment is to design the experiment to be most efficient for a best guess of the parameter values. A design which is optimal for a best guess however may not be efficie...
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A traditional way to design a binary response experiment is to design the experiment to be most efficient for a best guess of the parameter values. A design which is optimal for a best guess however may not be efficient for parameter values close to that best guess. We propose designs which formally account for the prior uncertainty in the parameter values. A design for a situation where the best guess has substantial uncertainty attached to itis very different from a design for a situation where approximate values of the parameters are known. We derive a general theory for concave design critria for non-linear models and then apply the theory to logistic regression. Designs found by numerical optimization are examined for a range of prior distributions and a range of criteria. The theoretical results are used to verify that the designs are indeed optimal.
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