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Minimum Description Length Principle.

Jorma Rissanen
- pp 666-668
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The article was published on 2010-01-01 and is currently open access. It has received 748 citations till now. The article focuses on the topics: Minimum description length.

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A survey of cross-validation procedures for model selection

TL;DR: In this paper, a survey on the model selection performances of cross-validation procedures is presented, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results, and guidelines are provided for choosing the best crossvalidation procedure according to the particular features of the problem in hand.
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Rényi Divergence and Kullback-Leibler Divergence

TL;DR: In particular, the Renyi divergence of order 1 equals the Kullback-Leibler divergence as discussed by the authors, and the relation of the special order 0 to the Gaussian dichotomy and contiguity is discussed.
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Boosting: Foundations and Algorithms

TL;DR: This book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics.
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A Tutorial on Bayesian Nonparametric Models

TL;DR: This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.
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Philosophy and the practice of Bayesian statistics

TL;DR: The authors argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism, and examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision.