Download or read book Probabilistic Reasoning Using General Forms of Conditional Independence written by Manon J. Sanscartier and published by . This book was released on 2002 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Theory of Conditional Information for Probabilistic Inference in Intelligent Systems 1 Interval of Events Approach written by and published by . This book was released on 1994 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introductory Statistics written by Douglas S. Shafer and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probabilistic Reasoning for Complex Systems written by Avrom J. Pfeffer and published by . This book was released on 1999 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probabilistic Conditional Independence Structures written by Milan Studeny and published by Springer Science & Business Media. This book was released on 2006-06-22 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Probabilistic Logic in a Coherent Setting written by Giulianella Coletti and published by Springer Science & Business Media. This book was released on 2002-10-31 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Logic of Conditionals written by E.W. Adams and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction to Probability written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2008-07-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains a number of more advanced topics, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Download or read book Probabilistic Reasoning in Intelligent Systems written by Judea Pearl and published by Elsevier. This book was released on 2014-06-28 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Conditional Independence in Applied Probability written by P.E. Pfeiffer and published by Springer Science & Business Media. This book was released on 2013-03-07 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Probabilistic Reasoning and Decision Making in Sensory Motor Systems written by Pierre Bessière and published by Springer Science & Business Media. This book was released on 2008-05-15 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filtering, modelling and mapping, with applications in a number of different contexts.
Download or read book Probabilistic Logic in a Coherent Setting written by Giulianella Coletti and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1268 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Download or read book Probabilistic Reasoning in Multiagent Systems written by Yang Xiang and published by Cambridge University Press. This book was released on 2002-08-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.
Download or read book Uncertainty in Artificial Intelligence written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Download or read book Symbolic and Quantitative Approaches to Reasoning with Uncertainty written by Salem Benferhat and published by Springer. This book was released on 2003-06-30 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001. The 68 revised full papers presented together with three invited papers were carefully reviewed and selected from over a hundred submissions. The book offers topical sections on decision theory, partially observable Markov decision processes, decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.
Download or read book Sensor Networks and Signal Processing written by Sheng-Lung Peng and published by Springer Nature. This book was released on 2020-07-16 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a collection of high-quality research papers presented at the 2nd International Conference on Sensor Networks and Signal Processing (SNSP 2019), held in Taiwan on November 19–22, 2019. It presents novel contributions in the areas of sensor and actuator networks, wireless networks, networking and protocols, security and privacy, wireless communications, distributed algorithms, Internet of Things, system modeling and performance analysis, fault tolerance/diagnostics, information management, data mining and analysis, embedded systems design, signal theory, signal and image processing, detection and estimation, spectral analysis, software developments, pattern recognition, data processing, remote sensing, big data, machine learning, information and coding theory, and industrial applications.
