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Toward Machine Wald

Owhadi, Houman and Scovel, Clint (2017) Toward Machine Wald. In: Handbook of Uncertainty Quantification. Springer , Cham, Switzerland, pp. 157-191. ISBN 978-3-319-12384-4.

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The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed by humans because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to think as humans, especially when faced with uncertainty, is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well-posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tends to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with decision theory, machine learning, Bayesian inference, stochastic optimization, robust optimization, optimal uncertainty quantification, and information-based complexity.

Item Type:Book Section
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URLURL TypeDescription Paper
Owhadi, Houman0000-0002-5677-1600
Scovel, Clint0000-0001-7757-3411
Alternate Title:Towards Machine Wald
Additional Information:© 2017 Springer International Publishing Switzerland.
Subject Keywords:Abraham Wald; Decision theory; Machine learning; Uncertainty quantification; Game theory
Record Number:CaltechAUTHORS:20160224-064915023
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64708
Deposited By: Ruth Sustaita
Deposited On:24 Feb 2016 18:19
Last Modified:03 Oct 2019 09:40

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