Probabilistic Reasoning Using General Forms of Conditional Independence PDF Download
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Author: Manon J. Sanscartier
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 326
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Book Description
Author: Manon J. Sanscartier
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 326
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 27
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Book Description
This paper emphasizes the need to develop further probability theory at the service of probabilistic intelligent systems. In the field of probabilistic systems, the causal relationships among variables of interest are viewed as if-then (or production) rules whose certainty factors are quantified as conditional probabilities. With some additional assumptions about the variables of interest, such as conditional independence, standard probability theory can be applied to carry out the reasoning processes. In more general situations, in which all information (in the premises as well as the conclusions) is in unconditional and conditional form-or in only conditional form-current probabilistic machinery requires more development to cope with this new situation. After identifying typical situations, we present a theory of conditional information in the form of the new concept of 'conditional events, ' compatible with all conditional probability quantifications. We specify applications of this theory to various problems in intelligent systems. The approach taken here to conditional events is through intervals of events. Applied research, Basic research, Command, Control and Communications.
Author: Douglas S. Shafer
Publisher:
ISBN: 9781453388945
Category : Mathematical statistics
Languages : en
Pages : 0
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Book Description
Author: Milan Studeny
Publisher: Springer Science & Business Media
ISBN: 1846280834
Category : Computers
Languages : en
Pages : 292
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Book Description
Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach. The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.
Author: Avrom J. Pfeffer
Publisher:
ISBN:
Category :
Languages : en
Pages : 652
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Book Description
Author: Giulianella Coletti
Publisher: Springer Science & Business Media
ISBN: 9781402009174
Category : Philosophy
Languages : en
Pages : 304
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Book Description
The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a `flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible `outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a way that avoids all the inconsistencies related to logical dependence (so that a theory referring to graphical models more general than those usually considered in bayesian networks can be derived). Moreover, it is possible to encompass other approaches to uncertain reasoning, such as fuzziness, possibility functions, and default reasoning. The book is kept self-contained, provided the reader is familiar with the elementary aspects of propositional calculus, linear algebra, and analysis.
Author: E.W. Adams
Publisher: Springer Science & Business Media
ISBN: 940157622X
Category : Philosophy
Languages : en
Pages : 173
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Book Description
Of the four chapters in this book, the first two discuss (albeit in consider ably modified form) matters previously discussed in my papers 'On the Logic of Conditionals' [1] and 'Probability and the Logic of Conditionals' [2], while the last two present essentially new material. Chapter I is relatively informal and roughly parallels the first of the above papers in discussing the basic ideas of a probabilistic approach to the logic of the indicative conditional, according to which these constructions do not have truth values, but they do have probabilities (equal to conditional probabilities), and the appropriate criterion of soundness for inferences involving them is that it should not be possible for all premises of the inference to be probable while the conclusion is improbable. Applying this criterion is shown to have radically different consequences from the orthodox 'material conditional' theory, not only in application to the standard 'fallacies' of the material conditional, but to many forms (e. g. , Contraposition) which have hitherto been regarded as above suspi cion. Many more applications are considered in Chapter I, as well as certain related theoretical matters. The chief of these, which is the most important new topic treated in Chapter I (i. e.
Author: Judea Pearl
Publisher: Elsevier
ISBN: 0080514898
Category : Computers
Languages : en
Pages : 573
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Book Description
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Author: P.E. Pfeiffer
Publisher: Springer Science & Business Media
ISBN: 1461263352
Category : Science
Languages : en
Pages : 160
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Book Description
It would be difficult to overestimate the importance of stochastic independence in both the theoretical development and the practical appli cations of mathematical probability. The concept is grounded in the idea that one event does not "condition" another, in the sense that occurrence of one does not affect the likelihood of the occurrence of the other. This leads to a formulation of the independence condition in terms of a simple "product rule," which is amazingly successful in capturing the essential ideas of independence. However, there are many patterns of "conditioning" encountered in practice which give rise to quasi independence conditions. Explicit and precise incorporation of these into the theory is needed in order to make the most effective use of probability as a model for behavioral and physical systems. We examine two concepts of conditional independence. The first concept is quite simple, utilizing very elementary aspects of probability theory. Only algebraic operations are required to obtain quite important and useful new results, and to clear up many ambiguities and obscurities in the literature.
Author: Giulianella Coletti
Publisher: Springer Science & Business Media
ISBN: 9401004749
Category : Philosophy
Languages : en
Pages : 285
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Book Description
The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a `flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible `outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a way that avoids all the inconsistencies related to logical dependence (so that a theory referring to graphical models more general than those usually considered in bayesian networks can be derived). Moreover, it is possible to encompass other approaches to uncertain reasoning, such as fuzziness, possibility functions, and default reasoning. The book is kept self-contained, provided the reader is familiar with the elementary aspects of propositional calculus, linear algebra, and analysis.
