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 particul… Mehr…
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. Books > Statistics eBook, Springer Shop<
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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 particul… Mehr…
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., Physica<
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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 particul… Mehr…
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. Books > Statistics eBook, Springer Shop<
new in stock. Versandkosten:zzgl. Versandkosten. (EUR 0.00)
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 particul… Mehr…
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., Physica<
Nr. 978-3-642-51746-4. Versandkosten:Worldwide free shipping, , DE. (EUR 0.00)
*A Mathematical Theory of Arguments for Statistical Evidence* / pdf eBook für 53.49 € / Aus dem Bereich: eBooks, Fachthemen & Wissenschaft, Mathematik Medien > Bücher nein eBook als pdf e… Mehr…
*A Mathematical Theory of Arguments for Statistical Evidence* / pdf eBook für 53.49 € / Aus dem Bereich: eBooks, Fachthemen & Wissenschaft, Mathematik Medien > Bücher nein eBook als pdf eBooks > Fachthemen & Wissenschaft > Mathematik, Physica-Verlag HD<
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Detailangaben zum Buch - A Mathematical Theory of Arguments for Statistical Evidence
EAN (ISBN-13): 9783642517464 Erscheinungsjahr: 2013 Herausgeber: Physica-Verlag HD
Buch in der Datenbank seit 2016-12-22T17:41:28+01:00 (Berlin) Detailseite zuletzt geändert am 2024-04-10T12:11:28+02:00 (Berlin) ISBN/EAN: 9783642517464
ISBN - alternative Schreibweisen: 978-3-642-51746-4 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: monney, andré paul Titel des Buches: evidence
Daten vom Verlag:
Autor/in: Paul-Andre Monney Titel: Contributions to Statistics; A Mathematical Theory of Arguments for Statistical Evidence Verlag: Physica; Physica 154 Seiten Erscheinungsjahr: 2013-04-18 Heidelberg; DE Sprache: Englisch 55,00 € (DE)
EA; E107; eBook; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; regression; valuation; value-at-risk; C; Statistics in Business, Management, Economics, Finance, Insurance; Computational Mathematics and Numerical Analysis; Mathematics and Statistics; Wirtschaftswissenschaft, Finanzen, Betriebswirtschaft und Management; Numerische Mathematik; BC
1. The Theory of Generalized Functional Models.- 2. The Plausibility and Likelihood Functions.- 3. Hints on Continuous Frames and Gaussian Linear Systems.- 4. Assumption-Based Reasoning with Classical Regression Models.- 5. Assumption-Based Reasoning with General Gaussian Linear Systems.- 6. Gaussian Hints as a Valuation System.- 7. Local Propagation of Gaussian Hints.- 8. Application to the Kaiman Filter.- References.
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