A Mathematical Theory of Arguments for Statistical Evidence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg
Physica-Verlag HD
2003
|
| Schriftenreihe: | Contributions to Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | The subject of this book is the reasoning under uncertainty based on sta tistical evidence, where the word reasoning is taken to mean searching for arguments in favor or against particular hypotheses of interest. The kind of reasoning we are using is composed of two aspects. The first one is inspired from classical reasoning in formal logic, where deductions are made from a knowledge base of observed facts and formulas representing the domain spe cific knowledge. In this book, the facts are the statistical observations and the general knowledge is represented by an instance of a special kind of sta tistical models called functional models. The second aspect deals with the uncertainty under which the formal reasoning takes place. For this aspect, the theory of hints [27] is the appropriate tool. Basically, we assume that some uncertain perturbation takes a specific value and then logically eval uate the consequences of this assumption. The original uncertainty about the perturbation is then transferred to the consequences of the assumption. This kind of reasoning is called assumption-based reasoning. Before going into more details about the content of this book, it might be interesting to look briefly at the roots and origins of assumption-based reasoning in the statistical context. In 1930, R. A. Fisher [17] defined the notion of fiducial distribution as the result of a new form of argument, as opposed to the result of the older Bayesian argument |
| Beschreibung: | 1 Online-Ressource (XIII, 154 p) |
| ISBN: | 9783642517464 9783790815276 |
| ISSN: | 1431-1968 |
| DOI: | 10.1007/978-3-642-51746-4 |
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Datensatz im Suchindex
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| author | Monney, Paul-André |
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| doi_str_mv | 10.1007/978-3-642-51746-4 |
| format | Electronic eBook |
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| institution | BVB |
| isbn | 9783642517464 9783790815276 |
| issn | 1431-1968 |
| language | English |
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| spelling | Monney, Paul-André Verfasser aut A Mathematical Theory of Arguments for Statistical Evidence by Paul-André Monney Heidelberg Physica-Verlag HD 2003 1 Online-Ressource (XIII, 154 p) txt rdacontent c rdamedia cr rdacarrier Contributions to Statistics 1431-1968 The subject of this book is the reasoning under uncertainty based on sta tistical evidence, where the word reasoning is taken to mean searching for arguments in favor or against particular hypotheses of interest. The kind of reasoning we are using is composed of two aspects. The first one is inspired from classical reasoning in formal logic, where deductions are made from a knowledge base of observed facts and formulas representing the domain spe cific knowledge. In this book, the facts are the statistical observations and the general knowledge is represented by an instance of a special kind of sta tistical models called functional models. The second aspect deals with the uncertainty under which the formal reasoning takes place. For this aspect, the theory of hints [27] is the appropriate tool. Basically, we assume that some uncertain perturbation takes a specific value and then logically eval uate the consequences of this assumption. The original uncertainty about the perturbation is then transferred to the consequences of the assumption. This kind of reasoning is called assumption-based reasoning. Before going into more details about the content of this book, it might be interesting to look briefly at the roots and origins of assumption-based reasoning in the statistical context. In 1930, R. A. Fisher [17] defined the notion of fiducial distribution as the result of a new form of argument, as opposed to the result of the older Bayesian argument Statistics Computer science / Mathematics Economics / Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Computational Mathematics and Numerical Analysis Informatik Mathematik Statistik Wirtschaft Dempster-Shafer-Theorie (DE-588)4395834-5 gnd rswk-swf Unsicheres Schließen (DE-588)4361044-4 gnd rswk-swf Unsicheres Schließen (DE-588)4361044-4 s Dempster-Shafer-Theorie (DE-588)4395834-5 s 1\p DE-604 https://doi.org/10.1007/978-3-642-51746-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Monney, Paul-André A Mathematical Theory of Arguments for Statistical Evidence Statistics Computer science / Mathematics Economics / Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Computational Mathematics and Numerical Analysis Informatik Mathematik Statistik Wirtschaft Dempster-Shafer-Theorie (DE-588)4395834-5 gnd Unsicheres Schließen (DE-588)4361044-4 gnd |
| subject_GND | (DE-588)4395834-5 (DE-588)4361044-4 |
| title | A Mathematical Theory of Arguments for Statistical Evidence |
| title_auth | A Mathematical Theory of Arguments for Statistical Evidence |
| title_exact_search | A Mathematical Theory of Arguments for Statistical Evidence |
| title_full | A Mathematical Theory of Arguments for Statistical Evidence by Paul-André Monney |
| title_fullStr | A Mathematical Theory of Arguments for Statistical Evidence by Paul-André Monney |
| title_full_unstemmed | A Mathematical Theory of Arguments for Statistical Evidence by Paul-André Monney |
| title_short | A Mathematical Theory of Arguments for Statistical Evidence |
| title_sort | a mathematical theory of arguments for statistical evidence |
| topic | Statistics Computer science / Mathematics Economics / Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Computational Mathematics and Numerical Analysis Informatik Mathematik Statistik Wirtschaft Dempster-Shafer-Theorie (DE-588)4395834-5 gnd Unsicheres Schließen (DE-588)4361044-4 gnd |
| topic_facet | Statistics Computer science / Mathematics Economics / Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Computational Mathematics and Numerical Analysis Informatik Mathematik Statistik Wirtschaft Dempster-Shafer-Theorie Unsicheres Schließen |
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