Riassunto:
This volume records the proceedings of the Fourteenth International Workshop of 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 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.
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.
Contenuti:
Preface. Applications. Flow and Diffusion Images from Bayesian Spectral Analysis of Motion-Encoded NRM Data; E.J. Fordham, et al. Bayesian Estimation of MR Images from Incomplete Raw Data; G.J. Marseille, et al. Quantified Maximum Entropy and Biological EPR Spectra; S.M. Glidewell, et al. The Vital Importance of Prior Information for the Decomposition of Ion Scattering Spectroscopy Data; R. Fischer, et al. Bayesian Consideration of the Tomography Problem; W. von der Linden. Using MaxEnt to Determine Nuclear Level Densities; N.J. Davidson, et al. A Fresh Look at Model Selection in Inverse Scattering; V.A. Macaulay, B. Buck. The Maximum Entropy Method in Small-Angle Scattering: S. Hansen, J.J. Müller. Maximum Entropy Multi-Resolution EM Tomography by Adaptive Subdivision; L.-H. Zou, et al. High Resolution Image Construction from IRAS Survey - Parallelization and Artifact Suppression; Y. Cao, T.A. Prince. Maximum Entropy Performance Analysis of Spread-Spectrum Multiple-Access Communications; F. Solms, et al. Noise Analysis in Optical Fibre Sensing: A Study Using the Maximum Entropy Method; L. Stergioulas, et al. Algorithms. AutoClass - A Bayesian Approach to Classification; J. Stutz, P. Cheeseman. Evolution Reviews of BayesCalc, a MATHEMATICA Package for Doing Bayesian Calculations; P. Desmedt, et al. Bayesian Inference for Basis Function Selection in Nonlinear System Identification Using Genetic Algorithms; V. Kadirkamanthan. Basics. The Meaning of the Word `Probability'; M. Tribus. The Hard Truth; K.M. Hanson, G.S. Cunningham. Are the Samples Doped - If So, How Much? A.J.M. Garrett. Confidence Intervals from One Observation; C. Rodríguez. Hypothesis Refinement;G.A. Vignaux, B. Robertson. Bayesian Density Estimation; S. Sibisi, J. Skilling. Scale-Invariant Markov Models for Bayesian Inversion of Linear Inverse Problems; S. Brette, et al. Foundations; Indifference, Independence and MaxEnt; M. Schramm, M. Greiner. The Maximum Entropy on the Mean Method, Noise and Sensitivity; J.-F. Bercher, et al. The Maximum Entropy Algorithm Applied to the Two-Dimensional Random Packing Problem; G.J. Daniell. Neural Networks. Bayesian Comparison of Models for Images; A.H. Barnett, D.J.C. MacKay. Interpolation models with Multiple Hyperparameters; D.J.C. MacKay, R. Takeuchi. Density Networks and Their Application to Protein Modelling; D.J.C. Mackay. The Cluster Expansion: A Hierarchical Density Model; S.P. Luttrell. The Partitioned Mixture Distribution: Multiple Overlapping Density Models; S.P. Luttrell. Physics. Generating Functional for the BBGKY Hierarchy and the N-Identical-Body Problem; S.F. Gull, A.J.M. Garrett. Entropies for Continua: Fluids and Magnetofluids; D. Montgomery, et al. A Logical Foundation for Real Thermodynamics; R.S. Silver. Index.
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