Riassunto
The Fifteenth International Workshop on Maximum Entropy and Bayesian Meth ods was held July 31-August 4, 1995 in Santa Fe, New Mexico, USA. St. John's College, located in the foothills of the Sangre de Cristo Mountains, provided a congenial setting for the Workshop. The relaxed atmosphere of the College, which was thoroughly enjoyed by all the attendees, stimulated free-flowing and thought ful discussions. Conversations continued at the social events, which included a reception at the Santa Fe Institute, a New Mexican dinner at Richard Silver's home, and an excursion to Los Alamos that ended with a mixed grill at FUller Lodge, the main hall of the former Los Alamos Ranch School. This volume represents the Proceedings of the Workshop. Articles on the tra ditional theme of the Workshop, application of the maximum-entropy principle and Bayesian methods for statistical inference in diverse areas of scientific re search, are contained in these Proceedings. As is tradition, the Workshop opened with a tutorial on Bayesian methods, lucidly presented by Peter Cheeseman and Wray Buntine (NASA AMES, Moffett Field). The lecture notes for their tutorial are available on the World Wide Web at http://bayes .lanl. gov / "'maxent/. In addition, several new thrusts for the Workshop are described below.
Contenuti
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
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
