Titlebook: Maximum Entropy and Bayesian Methods; Laramie, Wyoming, 19 W. T. Grandy,L. H. Schick Book 1991 Springer Science+Business Media Dordrecht 19

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The Role of Priors in Active Bayesian Learning in the Sequential Statistical Decision Frameworkrecent literature that in active learning situations (where the actions of the statistician, or the person learning about some parameters, influences the draws from the distribution about which he/she is learning), full learning may not take place. This challenges the use of the rational expectation

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The Principle of Maximum Entropy and the Difference between Risk and Uncertaintyt upon available information. The Principle of Maximum Entropy (PME) reduces an economic decision made under uncertainty to a decision made under risk. The PME accomplishes this because the necessary conditions for a probability distribution to achieve maximum entropy, given certain information, are

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The Photometric Accuracy of Astronomical Images Restored with the MEMSYS3 Codepe, simulations are described which test the photometric accuracy of the maximum entropy image restoration algorithm used in the code Memsys3 version 2. It is found that at the faintest brightness levels, while the code can recover blurred out point sources, they are recovered systematically too fai

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Computational Problems and Signal Processing in SETI. distinct signal paths for a typical observation) be analyzed in real time by special-purpose hardware. Even though the SETI system design is not based on maximum entropy and Bayesian methods (partly due to the real-time processing constraint) it is expected that enough data will be saved to be abl

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Applications of Maxent to Quantum Monte Carloynamical properties of quantum many-body systems by Monte Carlo methods, which is termed the “Analytic Continuation Problem.” For the Anderson model of dilute magnetic impurities in metals, we obtain spectral functions and transport coefficients which obey “Kondo Universality.”

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The Application of Maximum Entropy Signal Processing to Ultrasonic Surface Parameterisation rough surfaces. The aim is to establish a new technique for surface parameterisation by obtaining and analysing the height (or amplitude) distribution functions of rough surfaces. Data obtained from an experimental system is inherently noisy: Linear signal processing techniques cannot produce a noi