Probabilistic Reasoning In Intelligent Systems

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Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Elsevier
Page : 552 pages
File Size : 43,6 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9780080514895

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Probabilistic Reasoning in Intelligent Systems by Judea Pearl Pdf

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.

Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Morgan Kaufmann
Page : 576 pages
File Size : 40,8 Mb
Release : 1988-09
Category : Computers
ISBN : 1558604790

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Probabilistic Reasoning in Intelligent Systems by Judea Pearl Pdf

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.

Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Unknown
Page : 552 pages
File Size : 42,8 Mb
Release : 2014
Category : Mathematics
ISBN : OCLC:1105770351

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Probabilistic Reasoning in Intelligent Systems by Judea Pearl Pdf

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.

Probabilistic Reasoning in Multiagent Systems

Yang Xiang
Probabilistic Reasoning in Multiagent Systems Book PDF

Author : Yang Xiang
Publisher : Cambridge University Press
Page : 310 pages
File Size : 54,6 Mb
Release : 2002-08-26
Category : Computers
ISBN : 9781139434461

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Probabilistic Reasoning in Multiagent Systems by Yang Xiang Pdf

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.

Representing and Reasoning with Probabilistic Knowledge

Fahiem Bacchus
Representing and Reasoning with Probabilistic Knowledge Book PDF

Author : Fahiem Bacchus
Publisher : Cambridge, Mass. : MIT Press
Page : 264 pages
File Size : 49,7 Mb
Release : 1990
Category : Computers
ISBN : UOM:39015021630440

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Representing and Reasoning with Probabilistic Knowledge by Fahiem Bacchus Pdf

Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.

Computational Learning and Probabilistic Reasoning

Alexander Gammerman
Computational Learning and Probabilistic Reasoning Book PDF

Author : Alexander Gammerman
Publisher : John Wiley & Sons
Page : 352 pages
File Size : 46,8 Mb
Release : 1996-08-06
Category : Computers
ISBN : UOM:39015037793497

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Computational Learning and Probabilistic Reasoning by Alexander Gammerman Pdf

Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.

Probabilistic Reasoning in Expert Systems

Richard E. Neapolitan
Probabilistic Reasoning in Expert Systems Book PDF

Author : Richard E. Neapolitan
Publisher : CreateSpace
Page : 448 pages
File Size : 46,7 Mb
Release : 2012-06-01
Category : Computers
ISBN : 1477452540

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Probabilistic Reasoning in Expert Systems by Richard E. Neapolitan Pdf

This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.

Modeling and Reasoning with Bayesian Networks

Adnan Darwiche
Modeling and Reasoning with Bayesian Networks Book PDF

Author : Adnan Darwiche
Publisher : Cambridge University Press
Page : 561 pages
File Size : 43,8 Mb
Release : 2009-04-06
Category : Computers
ISBN : 9780521884389

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Modeling and Reasoning with Bayesian Networks by Adnan Darwiche Pdf

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Systematic Introduction to Expert Systems

Frank Puppe
Systematic Introduction to Expert Systems Book PDF

Author : Frank Puppe
Publisher : Springer Science & Business Media
Page : 353 pages
File Size : 51,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642779718

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Systematic Introduction to Expert Systems by Frank Puppe Pdf

At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.

Probabilistic Networks and Expert Systems

Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Probabilistic Networks and Expert Systems Book PDF

Author : Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 52,9 Mb
Release : 2007-07-16
Category : Computers
ISBN : 0387718230

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Probabilistic Networks and Expert Systems by Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Pdf

Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

Ning Zhong,Setsuo Ohsuga
New Directions in Rough Sets Data Mining and Granular Soft Computing Book PDF

Author : Ning Zhong,Setsuo Ohsuga
Publisher : Springer
Page : 566 pages
File Size : 48,6 Mb
Release : 2004-06-22
Category : Mathematics
ISBN : 9783540480617

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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing by Ning Zhong,Setsuo Ohsuga Pdf

This book constitutes the refereed proceedings of the 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC'99, held in Yamaguchi, Japan, in November 1999. The 45 revised regular papers and 15 revised short papers presented together with four invited contributions were carefully reviewed and selected from 89 submissions. The book is divided into sections on rough computing: foundations and applications, rough set theory and applications, fuzzy set theory and applications, nonclassical logic and approximate reasoning, information granulation and granular computing, data mining and knowledge discovery, machine learning, and intelligent agents and systems.

Transformers for Natural Language Processing

Denis Rothman
Transformers for Natural Language Processing Book PDF

Author : Denis Rothman
Publisher : Packt Publishing Ltd
Page : 385 pages
File Size : 41,5 Mb
Release : 2021-01-29
Category : Computers
ISBN : 9781800568631

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Transformers for Natural Language Processing by Denis Rothman Pdf

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

Approximate Reasoning in Intelligent Systems, Decision and Control

E. Sanchez,L.A. Zadeh
Approximate Reasoning in Intelligent Systems Decision and Control Book PDF

Author : E. Sanchez,L.A. Zadeh
Publisher : Pergamon
Page : 216 pages
File Size : 54,8 Mb
Release : 1987-03-31
Category : Computers
ISBN : UOM:39015012445311

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Approximate Reasoning in Intelligent Systems, Decision and Control by E. Sanchez,L.A. Zadeh Pdf

Documents realistic applications of approximate reasoning techniques, with emphasis placed on operational systems. The papers presented explore new areas of practical decision-making and control systems by considering important aspects of fuzzy logic theory and the latest developments in the field of expert systems. Specific fields of application covered include modelling and control, management, planning, diagnostics, finance and software. Contains 12 papers.

Causality, Probability, and Time

Samantha Kleinberg
Causality Probability and Time Book PDF

Author : Samantha Kleinberg
Publisher : Cambridge University Press
Page : 269 pages
File Size : 41,5 Mb
Release : 2013
Category : Computers
ISBN : 9781107026483

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Causality, Probability, and Time by Samantha Kleinberg Pdf

Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.

Bayesian Rationality

Mike Oaksford,Nick Chater
Bayesian Rationality Book PDF

Author : Mike Oaksford,Nick Chater
Publisher : Oxford University Press
Page : 342 pages
File Size : 49,8 Mb
Release : 2007-02-22
Category : Philosophy
ISBN : 9780198524496

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Bayesian Rationality by Mike Oaksford,Nick Chater Pdf

For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.