Increasing generalizability via the principle of minimum description length
Keyword(s):
Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these concerns by filtering noise from the observed data and consequently increasing generalizability to unseen data.
2002 ◽
Vol 34
(4)
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pp. 417-427
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2009 ◽
Vol 18
(3)
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pp. 241-269
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2012 ◽
Vol 4
(20)
◽
pp. 82-88