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A Mathematical Theory of Arguments for Statistical Evidence
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Catalogue Information
Field name
Details
Dewey Class
330.015195
Title
A Mathematical Theory of Arguments for Statistical Evidence ([EBook] /) / by Paul-André Monney.
Author
Monney, Paul-André
Other name(s)
SpringerLink (Online service)
Publication
Heidelberg : : Physica-Verlag HD : : Imprint: Physica, , 2003.
Physical Details
XIII, 154 p. : online resource.
Series
Contributions to Statistics
1431-1968
ISBN
9783642517464
Summary Note
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.:
Contents note
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.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site
http://dx.doi.org/10.1007/978-3-642-51746-4
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Subject References:
Computational Mathematics and Numerical Analysis
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Computer mathematics
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Statistics
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Statistics for Business/Economics/Mathematical Finance/Insurance
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Authors:
Monney, Paul-André
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SpringerLink (Online service)
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Series:
Contributions to Statistics
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Classification:
330.015195
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