• Speaker: Dr Barrie Stokes, School of Medicine and Public Health, The University of Newcastle
  • Title: Maximum Entropy Alternatives to Pearson Family Distributions
  • Location: Room V101, Mathematics Building (Callaghan Campus) The University of Newcastle
  • Time and Date: 3:15 pm, Fri, 5th Aug 2011
  • Abstract:

    In the spirit of ET Jaynes' Maximum Entropy Principle, a (Bayesian) prior distribution conforming or constrained, say, to known moments should have the maximum entropy of all such distributions. At a previous MaxEnt conference [MaxEnt 2009, Oxford Mississippi] a method of obtaining MaxEnt univariate distributions under a variety of constraints was presented. The Mathematica function Interpolation[], normally used with numerical data, can also process "semi-symbolic" data, and Lagrange Multiplier equations were solved for a set of symbolic ordinates describing the required MaxEnt probability density function. We apply a developed version of this approach to finding MaxEnt distributions having prescribed beta1 (skewness squared) and beta2 (kurtosis) values, and compare the entropies of the MaxEnt distributions to those of the Pearson family distributions having the same beta1 and beta2. (This work was presented in poster form at the recent MaxEnt 2011 conference in Waterloo, Canada.)

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