A Comparison of Two Approaches: Maximum Entropy on the Mean (MEM) and Bayesian Estimation (BAYES) for Inverse Problems

2001/11/14 by Ali Mohammad‐Djafari, A. Mohammad-Djafari, Mohammad-Djafari, A.
Computer Science · Mathematics · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Statistical Mechanics and Entropy #Statistical and numerical algorithms #Statistics and Probability (physics.data-an) #physics.data-an

paper · pdf · doi:10.48550/arxiv.physics/0111122

Presented at MaxEnt96. Appeared in Proceedings of the Maximum Entropy Conference, Berg-en-Dal, South Africa, M. Sears, V. Nedeljkovic, N.E. Pendock and S. Sibisi (Ed.), pp 77-91

arxiv created 2001/11/14 · openalex publication_date 2001/11/14 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

To handle with inverse problems, two probabilistic approaches have been proposed: the maximum entropy on the mean (MEM) and the Bayesian estimation (BAYES). The main object of this presentation is to compare these two approaches which are in fact two different inference procedures to define the solution of an inverse problem as the optimizer of a compound criterion. Keywords: Inverse problems, Maximum Entropy on the Mean, Bayesian inference, Convex analysis.

Citations

• Lagrange Multipliers and Optimality

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