Maximum entropy and Bayesian methods. Proceedings of the 11th international workshop on maximum entropy and Bayesian methods of statistical analysis, MAXENT 11, Seattle, WA, USA, June 16-23, 1991 (Q1894236)

[The articles of this volume will be reviewed individually.] These workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this volume. There are tutorial papers, theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. The contributions contained in this volume present a state-of-the-art overview. Indexed articles: \textit{Jaynes, E. T.}, The Gibbs paradox, 1-21 [Zbl 0925.80001] \textit{Skilling, John}, Bayesian solution of ordinary differential equations, 23-37 [Zbl 0828.65078] \textit{MacKay, David J. C.}, Bayesian interpolation, 39-66 [Zbl 0833.62006] \textit{Bretthorst, G. Larry}, Estimating the ratio of two amplitudes in nuclear magnetic resonance data, 67-77 [Zbl 0925.62445] \textit{Gregory, P. C.; Loredo, T. J.}, A Bayesian method for the detection of a periodic signal of unknown shape and period, 79-103 [Zbl 0925.62026] \textit{Hardy, Michael}, Entropies of likelihood functions, 127-130 [Zbl 0829.62008] \textit{Mohammad-Djafari, Ali}, Maximum likelihood estimation of the Lagrange parameters of the maximum entropy distributions, 131-139 [Zbl 0925.62025] \textit{Wagensberg, J.; Pastor, R.}, Entropy of form and hierarchic organization, 141-151 [Zbl 1126.62303] \textit{Garrett, A. J. M.}, A Bayesian looks at the anthropic principle, 153-164 [Zbl 0900.00052] \textit{MacKay, David J. C.}, The evidence for neural networks, 165-183 [Zbl 0832.68081] \textit{Solana, Vicente}, Linking the plausible and demonstrative inferences, 195-120 [Zbl 0925.03158] \textit{Neal, Radford M.}, Bayesian mixture modeling, 197-211 [Zbl 0829.62033] \textit{Mohammad-Djafari, Ali}, A Matlab program to calculate the maximum entropy distributions, 221-233 [Zbl 0925.62007] \textit{Buck, Brian; Macaulay, Vincent A.}, Entropy and sunspots: Their bearing on time-series, 241-252 [Zbl 0925.62023] \textit{Richardson, John M.; Marsh, Kenneth A.}, Basic concepts in multisensor data fusion, 253-271 [Zbl 0925.62444] \textit{Garrett, A. J. M.; Fisher, D. J.}, Combining data from different experiments: Bayesian analysis and meta- analysis, 273-286 [Zbl 0829.62032] \textit{Charter, M. K.}, Modelling drug behaviour in the body with MaxEnt, 287-302 [Zbl 0828.92017] \textit{Lund, Jay R.; Englehardt, James D.}, Information entropy and dose-response functions for risk analysis, 303-312 [Zbl 0925.62468] \textit{Hanson, Kenneth M.}, Making binary decisions based on the posterior probability distribution associated with tomographic reconstructions, 313-326 [Zbl 0925.92083] \textit{Styerwalt, D.; Heidbreder, G.}, On a Bayesian approach to coherent radar imaging, 377-396 [Zbl 0829.62098] \textit{Sibisi, Sibusiso}, The grand canonical sampler for Bayesian integration, 423-432 [Zbl 0828.65022] \textit{Soofi, Ehsan S.}, Recent developments in information-theoretic statistical analysis, 433-444 [Zbl 0925.62024] \textit{Yoshimura, Jin; Waterman, Daniel}, A scientific concept of probability, 449-452 [Zbl 0828.60001] \textit{Larson, Everett G.}, Bayesian logic and statistical mechanics -- illustrated by a quantum \(\text{spin} 1/2\) ensemble, 453-466 [Zbl 0825.82008]