Building Models with Bayes

ORAL

Abstract

The whole of modern Bayesian statistical methods is founded on the simple idea of Bayes rule, stated by the Reverend Thomas Bayes, and presented in 1763. Bayes rule is merely a simple statement of conditional probablility but can be used to make strong inferences. However, the application of Bayes rule to all but the simplest problems requires significant computation. As a result, Baysian-based approaches have been largely impractical until high-speed computing became inexpensive in the recent in the last 20 years or so. We discuss the general idea behind Bayes rule, how to use it to build physical models, and illustrate the approach for a simple case of lattice gas models.

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Authors

  • Gus Hart

  • Young-Yeal Song

    Brigham Young University, Colorado School of Mines, Colorado State University, Yale University, Department of Physics and Astronomy, Brigham Young University, Department of Mechanical Engineering, University of Utah, JILA, NIST and University of Colorado, University of Arizona, MIT, National Institute for Materials Science, Japan, Department of Mechanical Engineering, Brigham Young University, University of New Mexico, Iowa State University, Los Alamos National Lab XCP-2, Utah State University, Weber State University, New Mexico State University, College of Optical Science, University of Arizona, University of Nebraska, Lincoln, J.A. Woollam Co., U.S. Naval Research Laboratory, Arizona State University, BYU Nuclear Physics Group, Brigham Young University Physics and Astronomy, Los Alamos National Laboratory, University of Tsukuba, Japan, Colorado State University, NSF ERC for EUV science and technology, Center for Functional Nanomaterials, Brookhaven National Laboratory, University of Wisconsin, Madison, Utah Valley University, Argonne National Lab

  • Young-Yeal Song

    Brigham Young University, Colorado School of Mines, Colorado State University, Yale University, Department of Physics and Astronomy, Brigham Young University, Department of Mechanical Engineering, University of Utah, JILA, NIST and University of Colorado, University of Arizona, MIT, National Institute for Materials Science, Japan, Department of Mechanical Engineering, Brigham Young University, University of New Mexico, Iowa State University, Los Alamos National Lab XCP-2, Utah State University, Weber State University, New Mexico State University, College of Optical Science, University of Arizona, University of Nebraska, Lincoln, J.A. Woollam Co., U.S. Naval Research Laboratory, Arizona State University, BYU Nuclear Physics Group, Brigham Young University Physics and Astronomy, Los Alamos National Laboratory, University of Tsukuba, Japan, Colorado State University, NSF ERC for EUV science and technology, Center for Functional Nanomaterials, Brookhaven National Laboratory, University of Wisconsin, Madison, Utah Valley University, Argonne National Lab