Advanced statistics: the problem of overfitting and the method of minimal descriptions

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Published: 2011-12-10
DOI: https://doi.org/10.51987/Rev.Hosp.Ital.B.Aires.v31i4.962

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Pablo Argibay
Laboratorio de Aprendizaje Biológico y Artificial (LABAL) – Instituto de Ciencias Básicas y Medicina Experimental (ICBME) – Hospital Italiano de Buenos Aires, Argentina

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Issue

Vol. 31 No. 4 (2011): Revista del Hospital Italiano de Buenos Aires

Section

ICBME
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

1.
Argibay P. Advanced statistics: the problem of overfitting and the method of minimal descriptions. Rev Hosp Ital B.Aires [Internet]. 2011 Dec. 10 [cited 2026 Apr. 27];31(4):161-6. Available from: https://ojs.hospitalitaliano.org.ar/index.php/revistahi/article/view/962

References

Grünwald PD. The minimum description length principle. Cambridge, Mass: The MIT Press; 2007. (Adaptive computation and ma-chine learning series). DOI: https://doi.org/10.7551/mitpress/4643.001.0001

Grünwald PD, Myung IJ, Pitt, MA. Advances in minimum description length: theory and ap-plications. Cambridge, Mass: MIT Press; 2005. DOI: https://doi.org/10.7551/mitpress/1114.001.0001

Nannen V. The paradox of overfitting [Te-sis, Internet]. Amsterdam: Dutch National Re-search Institute for Mathematics and Informat-ics; 2003. [Consulta: 13/10/2011]. Disponible en: http://volker.nannen.com/work/mdl/

Rissanen J. Information and complexity in statistical modeling. New York: Springer; 2010

Rissanen J. Stochastic complexity in statis-tical inquiry. River Edge, NJ: World Scientific; 1989. (Series in computer science).

Tusell F. Complejidad estocástica [Inter-net]. [Consulta: 13/10/2011]. Disponible en: http://www.et.bs.ehu.es/~etptupaf/pub/papir-os/complex.pdf

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