Low-cost scalable discretization, prediction, and feature selection for complex systems-Reference-Cited by-同舟云学术

Low-cost scalable discretization, prediction, and feature selection for complex systems

Published:2020-01-31 Issue:5 Volume:6 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Gerber S.¹^ORCID,Pospisil L.²,Navandar M.¹,Horenko I.²^ORCID

Affiliation:

1. Center of Computational Sciences, Johannes-Gutenberg-University of Mainz, PhysMat/Staudingerweg 9, 55128 Mainz, Germany.

2. Faculty of Informatics, Universita della Svizzera Italiana, Via G. Buffi 13, 6900 Lugano Switzerland.

Abstract

The introduced data-driven tool allows simultaneous feature selection, model inference, and marked cost and quality gains.

Funder

Carl-Zeiss-Stiftung

Deutsche Forschungsgemeinschaft

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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3. Bayesian Inference for Causal Effects: The Role of Randomization

4. D. B. Rubin Bayesian Data Analysis (Chapman and Hall/CRC Texts in Statistical Science ed. 3 2013).

5. Ch. Schütte M. Sarich Metastability and Markov state models in molecular dynamics: Modeling analysis algorithmic approaches in Courant Lecture Notes in Mathematics (American Mathematical Soc. 2013) vol. 24.

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