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140122 ||| eng |
| 020 |
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|a 9789401587297
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| 100 |
1 |
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|a Heidbreder, Glenn R.
|e [editor]
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| 245 |
0 |
0 |
|a Maximum Entropy and Bayesian Methods Santa Barbara, California, U.S.A., 1993
|h Elektronische Ressource
|c edited by Glenn R. Heidbreder
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| 250 |
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|a 1st ed. 1996
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| 260 |
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|a Dordrecht
|b Springer Netherlands
|c 1996, 1996
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| 300 |
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|a X, 414 p
|b online resource
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| 505 |
0 |
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|a Tutorial -- An Introduction to Model Selection Using Probability Theory as Logic -- Bayesian Hyperparameters -- Hyperparameters: Optimize, or Integrate Out? -- What Bayes has to Say about the Evidence Procedure -- Reconciling Bayesian and Non-Bayesian Analysis -- Bayesian Robustness -- Bayesian Robustness: A New Look from Geometry -- Local Posterior Robustness with Parametric Priors: Maximum and Average Sensitivity -- Clustering -- Tree-Structured Clustering via the Minimum Cross Entropy Principle -- Inverse Problems -- A Scale-Invariant Bayesian Method to Solve Linear Inverse Problems -- Maximum Entropy Signal Transmission -- Quantum Probability Theory -- Maximum Quantum Entropy for Classical Density Functions -- Smoothing in Maximum Quantum Entropy -- Density Estimation by Maximum Quantum Entropy -- Philosophy -- Belief and Desire -- Computational Issues -- A Bayesian Genetic Algorithm for Calculating Maximum Entropy Distributions --
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| 505 |
0 |
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|a A Mathematica™ Package for Symbolic Bayesian Calculations -- A Multicriterion Evaluation of the Memsys5 Program for PET -- Parallel Maximum Entropy Reconstruction of PET Images -- Applications -- Bayesian Non-Linear Modeling for the Prediction Competition -- Bayesian Modeling and Classification of Neural Signals -- Estimators for the Cauchy Distribution -- Probability Theory and Multiexponential Signals: How Accurately Can the Parameters be Determined? -- Pixon-Based Image Reconstruction -- Super-Resolved Surface Reconstruction from Multiple Images -- Bayesian Analysis of Linear Phased-Array Radar -- Neural Network Image Deconvolution -- Bayesian Resolution of Closely Spaced Objects -- Ultrasonic Image Improvement through the Use of Bayesian Priors Which are Based on Adjacent Scanned Traces -- Application of Maxent to Inverse Photoemission Spectroscopy -- An EntropyEstimator Algorithm and Telecommunications Applications --
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| 505 |
0 |
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|a A Common Bayesian Approach to Multiuser Detection and Channel Equalization -- Thermostatics in Financial Economics -- Lessons from the New Evidence Scholarship -- How Good are a Set of Probability Predictions? The Expected Recommendation Loss (ERL) Scoring Rule
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| 653 |
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|a Physics and Astronomy
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| 653 |
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|a Statistics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Radiology
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| 653 |
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|a Probability Theory
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Physics
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| 653 |
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|a Statistics
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| 653 |
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|a Astronomy
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| 653 |
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|a Probabilities
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
0 |
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|a Fundamental Theories of Physics
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| 028 |
5 |
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|a 10.1007/978-94-015-8729-7
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| 856 |
4 |
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|u https://doi.org/10.1007/978-94-015-8729-7?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.2
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| 520 |
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|a Maximum entropy and Bayesian methods have fundamental, central roles in scientific inference, and, with the growing availability of computer power, are being successfully applied in an increasing number of applications in many disciplines. This volume contains selected papers presented at the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods. It includes an extensive tutorial section, and a variety of contributions detailing application in the physical sciences, engineering, law, and economics. Audience: Researchers and other professionals whose work requires the application of practical statistical inference
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