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Continuous MaxEnt distributions in Mathematica: a 'parameter-free' approach

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conference contribution
posted on 2025-05-09, 23:09 authored by Barrie James Stokes
Mathematica is used to develops a method of obtaining continuous MaxEnt distributions using the Lagrange Multiplier method that works directly in the discrete case. The continuous PDF (probability density function) is described by list of coordinates of the form {{abs₁, ord₁}, {abs₂, ord₂}, ... , {absn, ordn}}, where the abscissae absᵢ are specified numerically so as to define the domain of the PDF, and the ordinates ordᵢ are initially general symbolic variables that are assigned numeric values via the procedure to be described. In all the applications given here, n = 51. A set of Lagrange Multiplier equations is solved for the ordi, making use of the fact that the Mathematica function Interpolation[], which constructs interpolationFunction objects normally used for numeric data and for describing numerical solutions to differential equations, can operate on "semi-symbolic" data.

History

Source title

Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Name of conference

29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Location

Oxford, MI

Start date

2009-07-05

End date

2009-07-10

Pagination

292-301

Publisher

American Institute of Physics

Place published

Melville, NY

Language

  • en, English

College/Research Centre

Faculty of Science and Information Technology

School

School of Mathematical and Physical Sciences

Rights statement

© American Institute of Physics

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