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In Jae Myung
Researcher at Ohio State University
Publications - 24
Citations - 4897
In Jae Myung is an academic researcher from Ohio State University. The author has contributed to research in topics: Model selection & Minimum description length. The author has an hindex of 18, co-authored 24 publications receiving 4529 citations.
Papers
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Journal ArticleDOI
Tutorial on maximum likelihood estimation
TL;DR: The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles of MLE.
Journal ArticleDOI
The importance of complexity in model selection
TL;DR: It is shown that model selection based solely on the fit to observed data will result in the choice of an unnecessarily complex model that overfits the data, and thus generalizes poorly.
Journal ArticleDOI
Toward a method of selecting among computational models of cognition.
TL;DR: A method of selecting among mathematical models of cognition known as minimum description length is introduced, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen.
Book
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
TL;DR: Advances in Minimum Description Length is a sourcebook that will introduce the scientific community to the foundations of MDL, recent theoretical advances, and practical applications, and examples of how to apply MDL in research settings that range from bioinformatics and machine learning to psychology.
Journal ArticleDOI
When a good fit can be bad.
Mark A. Pitt,In Jae Myung +1 more
TL;DR: In this article, the authors focus on measuring the generalizability of a model's data-fitting abilities, which should be the goal of model selection, and introduce selection methods that factor in these properties when measuring fit.