Computational Learning And Probabilistic Reasoning

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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 : 54,7 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 Machine Learning

Kevin P. Murphy
Probabilistic Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 858 pages
File Size : 46,6 Mb
Release : 2022-03-01
Category : Computers
ISBN : 9780262369305

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Probabilistic Machine Learning by Kevin P. Murphy Pdf

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Reasoning with Probabilistic and Deterministic Graphical Models

Rina Dechter
Reasoning with Probabilistic and Deterministic Graphical Models Book PDF

Author : Rina Dechter
Publisher : Morgan & Claypool Publishers
Page : 193 pages
File Size : 42,7 Mb
Release : 2013-12-01
Category : Computers
ISBN : 9781627051989

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Reasoning with Probabilistic and Deterministic Graphical Models by Rina Dechter Pdf

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

Reasoning with Probabilistic and Deterministic Graphical Models

Rina Sreedharan
Reasoning with Probabilistic and Deterministic Graphical Models Book PDF

Author : Rina Sreedharan
Publisher : Springer Nature
Page : 185 pages
File Size : 55,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015830

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Reasoning with Probabilistic and Deterministic Graphical Models by Rina Sreedharan Pdf

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

Reasoning with Probabilistic and Deterministic Graphical Models

Rina Kraus
Reasoning with Probabilistic and Deterministic Graphical Models Book PDF

Author : Rina Kraus
Publisher : Springer Nature
Page : 187 pages
File Size : 54,8 Mb
Release : 2013-12-27
Category : Computers
ISBN : 9783031015663

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Reasoning with Probabilistic and Deterministic Graphical Models by Rina Kraus Pdf

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Elsevier
Page : 552 pages
File Size : 49,5 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.

Bayesian Reasoning and Machine Learning

David Barber
Bayesian Reasoning and Machine Learning Book PDF

Author : David Barber
Publisher : Cambridge University Press
Page : 739 pages
File Size : 52,9 Mb
Release : 2012-02-02
Category : Computers
ISBN : 9780521518147

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Bayesian Reasoning and Machine Learning by David Barber Pdf

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Statistical Relational Artificial Intelligence

Luc De Raedt,Kristian Kersting,Sriraam Natarajan,David Poole
Statistical Relational Artificial Intelligence Book PDF

Author : Luc De Raedt,Kristian Kersting,Sriraam Natarajan,David Poole
Publisher : Morgan & Claypool Publishers
Page : 259 pages
File Size : 50,8 Mb
Release : 2016-03-24
Category : Computers
ISBN : 9781681731803

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Statistical Relational Artificial Intelligence by Luc De Raedt,Kristian Kersting,Sriraam Natarajan,David Poole Pdf

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Artificial Intelligence

David L. Poole,Alan K. Mackworth
Artificial Intelligence Book PDF

Author : David L. Poole,Alan K. Mackworth
Publisher : Cambridge University Press
Page : 821 pages
File Size : 44,9 Mb
Release : 2017-09-25
Category : Computers
ISBN : 9781107195394

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Artificial Intelligence by David L. Poole,Alan K. Mackworth Pdf

Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Machine Learning

Kevin P. Murphy
Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1102 pages
File Size : 42,5 Mb
Release : 2012-08-24
Category : Computers
ISBN : 9780262018029

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Machine Learning by Kevin P. Murphy Pdf

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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 : 52,5 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.

Probability and Intuition

Sujith Vijay
Probability and Intuition Book PDF

Author : Sujith Vijay
Publisher : Independently Published
Page : 66 pages
File Size : 45,8 Mb
Release : 2020-09-12
Category : Electronic
ISBN : 9798685572875

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Probability and Intuition by Sujith Vijay Pdf

A guided tour of various topics in probability and statistics with applications in machine learning, economics, physics, biology and psychology. More emphasis has been given to the presentation of ideas than to rigorous mathematical analysis, so the book is accessible to anyone who is familiar with high school algebra and elementary calculus. Numerous examples of fallacies and apparent paradoxes in probabilistic reasoning are provided. It is hoped that the book will stimulate the interest of readers sufficiently to study the introduced topics in greater depth, as one can easily find advanced textbooks devoted to each of the chapters covered here.

Introduction to Statistical Relational Learning

Lise Getoor,Ben Taskar
Introduction to Statistical Relational Learning Book PDF

Author : Lise Getoor,Ben Taskar
Publisher : MIT Press
Page : 602 pages
File Size : 43,7 Mb
Release : 2019-09-22
Category : Computers
ISBN : 9780262538688

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Introduction to Statistical Relational Learning by Lise Getoor,Ben Taskar Pdf

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

Probabilistic and Causal Inference

Hector Geffner,Rina Dechter,Joseph Halpern
Probabilistic and Causal Inference Book PDF

Author : Hector Geffner,Rina Dechter,Joseph Halpern
Publisher : Morgan & Claypool
Page : 946 pages
File Size : 45,7 Mb
Release : 2022-03-10
Category : Computers
ISBN : 9781450395892

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Probabilistic and Causal Inference by Hector Geffner,Rina Dechter,Joseph Halpern Pdf

Professor Judea Pearl won the 2011 Turing Award “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.” This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988–2001), and causality, recent period (2002–2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl’s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

An Inductive Logic Programming Approach to Statistical Relational Learning

Kristian Kersting
An Inductive Logic Programming Approach to Statistical Relational Learning Book PDF

Author : Kristian Kersting
Publisher : IOS Press
Page : 258 pages
File Size : 44,5 Mb
Release : 2006
Category : Computers
ISBN : 1586036742

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An Inductive Logic Programming Approach to Statistical Relational Learning by Kristian Kersting Pdf

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.