Model selection for sequential designs in discrete finite systems using Bernstein kernels
We view sequential design as a model selection problem to determine which new observation is expected to be the most informative, given the existing set of observations. For estimating a probability distribution on a bounded interval, we use bounds constructed from kernel density estimators along wi... Full description
Main Author:  Nath, Madhurima 
Other Authors:  Eubank, Stephen 
Format:  Electronic Article 
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Quelle:  Cornell University 
ID:  Arxiv ID: 1807.06661 
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recordid:  arxiv1807.06661 
title:  Model selection for sequential designs in discrete finite systems using Bernstein kernels 
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description:  We view sequential design as a model selection problem to determine which new observation is expected to be the most informative, given the existing set of observations. For estimating a probability distribution on a bounded interval, we use bounds constructed from kernel density estimators along with the estimated density itself to estimate the information gain expected from each observation. We choose Bernstein polynomials for the kernel functions because they provide a complete set of basis functions for polynomials of finite degree and thus have useful convergence properties. We illustrate the method with applications to estimating network reliability polynomials, which give the probability of certain sets of configurations in finite, discrete stochastic systems. 
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source:  Cornell University 
identifier:  Arxiv ID: 1807.06661 
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