Maximum Entropy and Bayesian Methods - Info and Reading Options
By John Skilling
“Maximum Entropy and Bayesian Methods” Metadata:
- Title: ➤ Maximum Entropy and Bayesian Methods
- Author: John Skilling
“Maximum Entropy and Bayesian Methods” Subjects and Themes:
- Subjects: ➤ Bayesian statistical decision theory - Entropy (information theory) - Analytic Chemistry - Mathematics - Distribution (Probability theory) - Analytical biochemistry - Probability Theory and Stochastic Processes - Dynamical Systems and Complexity Statistical Physics - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences - Physical and theoretical Chemistry - Electrical engineering - Physical organic chemistry - Computer engineering - Measurement Science and Instrumentation
Edition Identifiers:
- The Open Library ID: OL20705480W
AI-generated Review of “Maximum Entropy and Bayesian Methods”:
"Maximum Entropy and Bayesian Methods" Description:
The Open Library:
This volume records the proceedings of the Fourteenth International Workshop on Maximum Entropy and Bayesian Methods, held in Cambridge, England from August 1-5, 1994. Throughout applied science, Bayesian inference is giving high quality results augmented with reliabilities in the form of probability values and probabilistic error bars. Maximum Entropy, with its emphasis on optimally selected results, is an important part of this. Across wide areas of spectroscopy and imagery, it is now realistic to generate clear results with quantified reliability. This power is underpinned with a foundation of solid mathematics. The annual Maximum Entropy Workshops have become the principal focus of developments in the field, and which capture the imaginative research that defines the state of the art in the subject. The breadth of application is seen in the thirty-three papers reproduced here, which are classified into subsections on Basics, Applications, Physics and Neural Networks. Audience: This volume will be of interest to graduate students and researchers whose work involves probability theory, neural networks, spectroscopic methods, statistical thermodynamics and image processing.
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