Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
1998
|
| Schriftenreihe: | Fundamental Theories of Physics, An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application
98 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people |
| Beschreibung: | 1 Online-Ressource (IX, 302 p) |
| ISBN: | 9789401150286 9789401061117 |
| DOI: | 10.1007/978-94-011-5028-6 |
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| 500 | |a This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people | ||
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| 650 | 4 | |a Computational complexity | |
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| 650 | 4 | |a Künstliche Intelligenz | |
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Datensatz im Suchindex
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| discipline | Physik Mathematik |
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| series2 | Fundamental Theories of Physics, An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application |
| spelling | Erickson, Gary J. Verfasser aut Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis edited by Gary J. Erickson, Joshua T. Rychert, C. Ray Smith Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis Dordrecht Springer Netherlands 1998 1 Online-Ressource (IX, 302 p) txt rdacontent c rdamedia cr rdacarrier Fundamental Theories of Physics, An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application 98 This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people Mathematics Coding theory Computational complexity Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Discrete Mathematics in Computer Science Coding and Information Theory Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik Rychert, Joshua T. Sonstige oth Smith, C. Ray Sonstige oth https://doi.org/10.1007/978-94-011-5028-6 Verlag Volltext |
| spellingShingle | Erickson, Gary J. Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis Mathematics Coding theory Computational complexity Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Discrete Mathematics in Computer Science Coding and Information Theory Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik |
| title | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis |
| title_alt | Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis |
| title_auth | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis |
| title_exact_search | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis |
| title_full | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis edited by Gary J. Erickson, Joshua T. Rychert, C. Ray Smith |
| title_fullStr | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis edited by Gary J. Erickson, Joshua T. Rychert, C. Ray Smith |
| title_full_unstemmed | Maximum Entropy and Bayesian Methods Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis edited by Gary J. Erickson, Joshua T. Rychert, C. Ray Smith |
| title_short | Maximum Entropy and Bayesian Methods |
| title_sort | maximum entropy and bayesian methods boise idaho usa 1997 proceedings of the 17th international workshop on maximum entropy and bayesian methods of statistical analysis |
| title_sub | Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis |
| topic | Mathematics Coding theory Computational complexity Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Discrete Mathematics in Computer Science Coding and Information Theory Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik |
| topic_facet | Mathematics Coding theory Computational complexity Artificial intelligence Distribution (Probability theory) Statistics Probability Theory and Stochastic Processes Statistics, general Discrete Mathematics in Computer Science Coding and Information Theory Artificial Intelligence (incl. Robotics) Künstliche Intelligenz Mathematik Statistik |
| url | https://doi.org/10.1007/978-94-011-5028-6 |
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