Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Beschreibung

The papers for these proceedings were peer reviewed. Bayesian inference and maximum entropy methods provide a framework for analyzing very complicated data sets. 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.

Autor*in

C.J. Williams

Themen in »Bayesian Inference and Maximum Entropy Methods in Science and Engineering«

Inference methods approximations complex systems inductive logic theory probability signal separation/restoration tomography and imaging

Stimmen zu »Bayesian Inference and Maximum Entropy Methods in Science and Engineering«

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