Inhaltsangabe
Preface. Reconstruction f the Probability Density Function Implicit in Option Prices from Incomplete and Noisy Data; R.J. Hawkins, et al. Model Selection and Parameter Estimation for Exponential Signals; A. Ramaswami, G.L. Bretthorst. Hierarchical Bayesian Time-Series Models; L.M. Berliner. Bayesian Time Series: Models and Computations for the Analysis of Time Series in the Physical Sciences; M. West. Maxent, Mathematics, and Information Theory; I. Csiszár. Bayesian Estimation of the Von Mises Concentration Parameter; D.L. Dowe, et al. A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks; D. Geiger, D. Heckerman. The Bootstrap is Inconsistent with Probability Theory; D.H. Wolpert. Data-Driven Priors for Hyperparameters in Regularization; D. Keren, M. Werman. Mixture Modeling to Incorporate Meaningful Constraints into Learning; I. Tchoumatchenko, J.-G. Ganascia. Maximum Entropy (Maxent) Method in Expert Systems and Intelligent Control: New Possibilities and Limitations; V. Kreinovich, et al. The De Finetti Transform; S.J. Press. Continuum Models for Bayesian Image Matching; J.C. Gee, P.D. Peralta. Mechanical Models as Priors in Bayesian Tomographic Reconstruction; A. Rangarajan, et al. The Bayes Inference Engine; K.M. Hanson, G.S. Cunningham. A Full Bayesian Approach for Inverse Problems; A. Mohammad- Djafari. Pixon-Based Multiresolution Image Reconstruction and Quantification of Image Information Content; R.C. Puetter. Bayesian Multimodal Evidence Computation by Adaptive Tempering MCMC; M.-D. Wu, W.J. Fitzgerald. Bayesian Inference and the Analytic Continuation of Imaginary- Time Quantum Monte Carlo Data; J.E. Gubermatis, et al. Spectral Properties from Quantum Monte CarloData: A Consistent Approach; R. Preuss, et al. An Application of Maximum Entropy Method to Dynamical Correlation Functions at Zero Temperature; H. Pang, et al. Chebyshev Moment Problems: Maximum Entropy and Kernel Polynomial Methods; R.N. Silver, et al. Cluster Expansions and Iterative Scaling for Maximum-Entropy Language Models; J.D. Lafferty, B. Suhm. A Maxent Tomography Method for Estimating Fish Densities in a Commercial Fishery; S. Lizamore, et al. Toward Optimal Observer Performance of Detection and Discrimination Tasks on Reconstructions from Sparse Data; R.F. Wagner, et al. Entropies for Dissipative Fluids and Magnetofluids without Discretization; D. Montgomery. On the Importance of &agr; Marginalization in Maximum Entropy; R. Fisher, et al. Quantum Mechanics as an Exotic Probability Theory; S. Youssef. Bayesian Parameter Estimation of Nuclear-Fusion Confinement Time Scaling Laws; V. Dose, et al. Hierarchical Segmentation of Range and Color Images Based on Bayesian Decision Theory; P. Boulanger. Priors on Measures; J. Skilling, S. Sibisi. Determining Whether Two Data Sets are from the Same Distribution; D.H. Wolpert. Occam's Razor for Parametric Families and Priors on the Space of Distributions; V. Balasubramanian. Skin and Maximum Entropy: A Hidden Complicity? B. Dubertret, et al. Predicting the Accuracy of Bayes Classifiers; R.R. Snapp. Maximum Entropy Analysis of Genetic Algorithms; J.L. Shapiro, et al. Data Fusion in the Field of Non Destructive Testing; S. Gautier, et al. Dual Statistical Mechanical Theory for Unsupervised And Supervised Learning; G. Deco, B. Schürmann. Complex Sinusoid Analysis by Bayesian Deconvolution of the Discrete Fourier Transform; F. Dublanchet, et al. Statistical Mechanics of Choice; P.S
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This volume contains the proceedings of the Fifteenth International Workshop on Maximum Entropy and Bayesian Methods, held in Santa Fe, New Mexico, U.S.A., from July 31-August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific inference in the natural and social sciences, engineering and medicine. Practical applications include, among others, parametric model fitting and model selection, ill-posed inverse problems, image reconstruction signal processing, decision making, and spectrum estimation. Fundamental applications include the common foundations for statistical inference, statistical physics and information theory. Specific sessions during the workshop focused on time series analysis, machine learning, deformable geometric models, and data analysis of Monte Carlo simulations, as well as reviewing the relation between maximum entropy and information theory. Audience: This book should be of interest to scientists, engineers, medical professionals, and others engaged in such topics as data analysis, statistical inference, image processing, and signal processing.
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