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| Preface | ||
| Tutorial | ||
| An Introduction to Model Selection Using Probability Theory as Logic | 1 | |
| Hyperparameters: Optimize, or Integrate Out? | 43 | |
| What Bayes Has to Say About the Evidence Procedure | 61 | |
| Reconciling Bayesian and Non-Bayesian Analysis | 79 | |
| Bayesian Robustness: A New Look From Geometry | 87 | |
| Local Posterior Robustness with Parametric Priors: Maximum and Average Sensitivity | 97 | |
| Tree-Structured Clustering Via the Minimum Cross Entropy Principle | 107 | |
| A Scale-Invariant Bayesian Method to Solve Linear Inverse Problems | 121 | |
| Maximum Entropy Signal Transmission | 135 | |
| Maximum Quantum Entropy for Classical Density Functions | 149 | |
| Smoothing in Maximum Quantum Entropy | 157 | |
| Density Estimation by Maximum Quantum Entropy | 161 | |
| Belief and Desire | 175 | |
| A Bayesian Genetic Algorithm for Calculating Maximum Entropy Distributions | 187 | |
| A Mathematica Package for Symbolic Bayesian Calculations | 197 | |
| A Multicriterion Evaluation of the MEMSYS5 Program for Pet | 205 | |
| Parallel Maximum Entropy Reconstruction of Pet Images | 213 | |
| Bayesian Non-Linear Modeling for the Prediction Competition | 221 | |
| Bayesian Modeling and Classification of Neural Signals | 235 | |
| Estimators for the Cauchy Distribution | 255 | |
| Probability Theory and Multiexponential Signals: How Accurately Can the Parameters be Determined? | 265 | |
| Pixon-Based Image Reconstruction | 275 | |
| Super-Resolved Surface Reconstruction from Multiple Images | 293 | |
| Bayesian Analysis of Linear Phased Array Radar | 309 | |
| Neural Network Image Deconvolution | 319 | |
| Bayesian Resolution of Closely Spaced Objects | 327 | |
| Ultrasonic Image Improvement Through the Use of Bayesian Priors Which are Based on Adjacent Scanned Traces | 339 | |
| Application of Maxent to Inverse Photoemission Spectroscopy | 343 | |
| An Entropy Estimator Algorithm and Telecommunications Applications | 351 | |
| A Common Bayesian Approach to Multiuser Detection and Channel Equalization | 365 | |
| Thermostatics in Financial Economics | 375 | |
| Lessons from the New Evidence Scholarship | 391 | |
| How Good are a Set of Probability Predictions? The Expected Recommendation Loss (ERL) Scoring Rule | 401 | |
| Index | 409 |
| Price | Condition | Delivery | Seller | Action |
| $271.00 | Digital |
| WonderClub |
Trey Mitchell
reviewed Maximum Entropy And Bayesian Methods Santa Barbara, California, U.S.A., 1993 on May 06, 2014
Maximum Entropy And Bayesian Methods Santa Barbara
Storie davvero intricate, ma divertenti! Questo libriccino contiene 10 piccoli enigmi matematici, che Carroll chiama "nodi" e alcuni sono effettivamente alla portata di tutti. Ti portano a ragionare, ma sicuramente la presenza delle soluzioni e dei commenti dell'autore stesso aiutano nello svolgimento, se ci sono dubbi. Altrimenti, si legge direttamente la soluzione e via! Infatti questi nodi venivano pubblicati periodicamente da Carroll su una rivista e i lettori avevano la possibilità di risolverli e inviare la loro risposta. Dopodiché, nel numero successivo, lui scriveva la risoluzione e stilava una classifica dei migliori e dei peggiori. Inutile dire quanto sarcasmo usasse per beffarsi di chi sbagliava! In conclusione, sia che ci si lasci coinvolgere nei ragionamenti matematici, sia che si voglia semplicemente scoprire un lato dell'autore meno conosciuto, è una piccola chicca da tenere in libreria per gli appassionati di Lewis Carroll.
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