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Improved Kullback-Leibler Upper Bound Baised on Convex Combination of k Rival Models
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Abdolreza Sayyareh *   |
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Abstract: (24048 Views) |
| In this paper we have established for the Kullback-Leibler divergence that the relative error is supperadditive. It shows that a mixture of k rival models gives a better upper bound for Kullback-Leibler divergence to model selection. In fact, it is shown that the mixed model introduce a model which is better than of the all rival models in the mixture or a model which is better than the worst rival model in the mixture. |
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| Keywords: Convex Combination, Geometric Mean, Kullback-Leibler Risk, Mixture of Models, Model Selection, Relative Error |
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Full-Text [PDF 1376 kb]
(3935 Downloads)
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Type of Study: Research |
Subject:
Statistical Inference
Received: 2012/04/19 | Accepted: 2012/04/20 | Published: 2020/02/18
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