2002Unpublished venueRequires access

Principle of Maximum Entropy for back analysis in geotechnical engineering

Bin Chen

Open publisher page 2 citations

Abstract

Based on the modern information theory, two objective functions of the maximum likelihood method and Bayes' Method are deduced with random process and the Principle of Maximum Entropy. The theoretical significance of the deduction is that the maximum likelihood method and Bayes' Method are reconsidered according to the data information, and that one more reference is added to the back analysis which was performed only based on data and algorithms, thus, providing a new method for research of indeterminateness of back analysis.

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What this paper is about

Based on the modern information theory, two objective functions of the maximum likelihood method and Bayes' Method are deduced with random process and the Principle of Maximum Entropy. The theoretical significance of the deduction is that the maximum likelihood method and Bayes' Method are reconsidered according to the data information, and that one more reference is added to the back analysis which was performed only based on data and algorithms, thus, providing a new method for research of indeterminateness of back analysis.

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Available abstract

Based on the modern information theory, two objective functions of the maximum likelihood method and Bayes' Method are deduced with random process and the Principle of Maximum Entropy. The theoretical significance of the deduction is that the maximum likelihood method and Bayes' Method are reconsidered according to the data information, and that one more reference is added to the back analysis which was performed only based on data and algorithms, thus, providing a new method for research of indeterminateness of back analysis.

Key concepts: Principle of maximum entropy, Bayes' theorem, Maximum likelihood, Entropy (arrow of time), Mathematics, Maximum entropy spectral estimation, Maximum entropy thermodynamics, Statistics

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