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The normalized maximum likelihood code length has been widely used in model selection, and its favorable properties, such as its consistency and the upper bound of its statistical risk, have been demonstrated.
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Y. M. Shtar’kov · 1987
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R. Nishii · 1988
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J. Rissanen, T. P. Speed, and B. Yu · 1992
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K. Yamanishi · 1992
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J. Rissanen · 1995
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J. Rissanen · 1996
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J. Rissanen · 1997
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A. W. Van der Vaart · 1998
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J. Rissanen · 2000
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P. D. Grünwald · 2007
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A linear-time algorithm for computing the multinomial stochastic complexity
P. Kontkanen and P. Myllymäki · 2007
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P. Kontkanen and P. Myllymäki · 2007
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P. Kontkanen and P. Myllymäki · 2008
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Bayesian network structure learning using factorized nml universal models
T. Roos, T. Silander, P. Kontkanen, and P. Myllymaki · 2008
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Efficient computation of normalized maximum likelihood codes for gaussian mixture models with its applications to clustering
S. Hirai and K. Yamanishi · 2013
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A tight excess risk bound via a unified pac-bayesian-rademacher-shtarkov-mdl complexity
P. D. Grünwald and N. A. Mehta · 2017
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