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BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 22nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

22nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Christopher J. Williams, University of Idaho, Division of Statistics, Moscow, ID, USA


AIP Conference Proceedings 659


Conference Location and Date: Moscow, Idaho, 3-7 August 2002

Subseries: Mathematical and Statistical Physics

Published April 2003; ISBN 0-7354-0119-5, One Volume, Print; 423 pages; 6 3/8 X 9 1/4 inches; Hardcover; $165.00

Readership: Physical and biological sciences data analysts, statisticians, and applied mathematicians.

The papers for these proceedings were peer reviewed. Bayesian inference and maximum entropy methods provide a framework for analyzing very complicated data sets. The areas of application range from signal processing to thermodynamics. Maximum entropy is a theoretical method used to develop data when little information is available. Image enhancement of unclear astronomical data and medical images can be clarified using these methods. The papers in this volume provide applications of these methods to problems such as medical imaging, weather prediction, intrusion detection, and modeling planetary nebulae. Other papers address foundational questions that underlie these methods. Topics include: estimation and inference; applications in physics; signal separation and classification; inductive logic theory; prior specification; and tutorials.

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