M
Miroslav Dudík
Researcher at Microsoft
Publications - 98
Citations - 28085
Miroslav Dudík is an academic researcher from Microsoft. The author has contributed to research in topics: Prediction market & Regret. The author has an hindex of 33, co-authored 98 publications receiving 23259 citations. Previous affiliations of Miroslav Dudík include Yahoo! & Carnegie Mellon University.
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Journal ArticleDOI
Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Journal ArticleDOI
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
Journal ArticleDOI
A statistical explanation of MaxEnt for ecologists
TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
Journal ArticleDOI
Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data
Steven J. Phillips,Miroslav Dudík,Jane Elith,Catherine H. Graham,Anthony Lehmann,John R. Leathwick,Simon Ferrier +6 more
TL;DR: It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.
Proceedings ArticleDOI
A maximum entropy approach to species distribution modeling
TL;DR: This work proposes the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features, and investigates the interpretability of models constructed using maxent.