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Maximum Entropy and Bayesian Methods pp 463–473Cite as

Maxent Applied To Linear Regression

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Part of the book series: Fundamental Theories of Physics ((FTPH,volume 39))

Abstract

Given sparse, unreplicated data of poor instrumental resolution, we determine the probability of linear models using orthogonal least squares regression and MAXENT with an ‘expert draftsman’ constraint. An information bound condition enables MAXENT inference for the reliability of evidence determining the probability distribution for observations of a constant.

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Author information

Authors and Affiliations

  1. The Heatherington, #211, 1421 Massachusetts Ave, 20005, NW Washington, DC, USA

    J. F. Cyranski

Authors
  1. J. F. Cyranski
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Editors and Affiliations

  1. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, USA

    Paul F. Fougère

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© 1990 Kluwer Academic Publishers

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Cyranski, J.F. (1990). Maxent Applied To Linear Regression. In: Fougère, P.F. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0683-9_32

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  • DOI: https://doi.org/10.1007/978-94-009-0683-9_32

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6792-8

  • Online ISBN: 978-94-009-0683-9

  • eBook Packages: Springer Book Archive

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