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Applied Computational Physics$
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Joseph F. Boudreau and Eric. S. Swanson

Print publication date: 2017

Print ISBN-13: 9780198708636

Published to Oxford Scholarship Online: February 2018

DOI: 10.1093/oso/9780198708636.001.0001

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Data modeling

Data modeling

Chapter:
(p.511) 16 Data modeling
Source:
Applied Computational Physics
Author(s):

Joseph F. Boudreau

Eric S. Swanson

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198708636.003.0016

A variety of techniques for extracting information from data are presented, from pedestrian approaches such as the centuries old linear least-squares fit, to elegant binned and unbinned likelihood fits. A treatment of statistical combination of data leads to an introduction to the powerful Kalman filter approach, used to determine optimal estimates of deterministic-stochastic systems. In experimental physics the Kalman filter is used estimate trajectories from data, but it also finds applications in industrial process control, and in the aeronautics and robots industries. These techniques typically rely on either analytic or numerical optimization of an objective function. Orthogonal series density estimation, a Fourier technique, is also discussed.

Keywords:   least squares, likelihood, orthogonal functions, Kalman filter, deterministic-stochastic systems, data modeling, fitting, minimization, Fourier technique

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