Bayesian Artificial Intelligence Second Edition
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Bayesian Artificial Intelligence Second Edition
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Author : Kevin B. Korb
language : en
Publisher: CRC Press
Release Date : 2010-12-16
Bayesian Artificial Intelligence Second Edition written by Kevin B. Korb and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-16 with Business & Economics categories.
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.
Bayesian Artificial Intelligence
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Author : Kevin B. Korb
language : en
Publisher: CRC Press
Release Date : 2010-12-16
Bayesian Artificial Intelligence written by Kevin B. Korb and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-16 with Business & Economics categories.
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors dis
Artificial Intelligence
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Author : Richard E. Neapolitan
language : en
Publisher: CRC Press
Release Date : 2018-03-12
Artificial Intelligence written by Richard E. Neapolitan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-12 with Computers categories.
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.
Machine Learning Second Edition
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2020
Machine Learning Second Edition written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, including deep learning, viewed through the lens of probabilistic modeling and Bayesian decision theory. This second edition has been substantially expanded and revised, incorporating many recent developments in the field. It has new chapters on linear algebra, optimization, implicit generative models, reinforcement learning, and causality; and other chapters on such topics as variational inference and graphical models have been significantly updated. The software for the book (hosted on github) is now implemented in Python rather than MATLAB, and uses state-of-the-art libraries including as scikit-learn, Tensorflow 2, and JAX.
Bayesian Networks
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Author : Olivier Pourret
language : en
Publisher: John Wiley & Sons
Release Date : 2008-04-30
Bayesian Networks written by Olivier Pourret and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-30 with Mathematics categories.
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Advanced Artificial Intelligence Second Edition
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Author : Shi Zhongzhi
language : en
Publisher: World Scientific
Release Date : 2019-08-05
Advanced Artificial Intelligence Second Edition written by Shi Zhongzhi and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-05 with Computers categories.
The joint breakthrough of big data, cloud computing and deep learning has made artificial intelligence (AI) the new focus in the international arena. AI is a branch of computer science, developing intelligent machine with imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behaviour.This comprehensive compendium, consisting of 15 chapters, captures the updated achievements of AI. It is completely revised to reflect the current researches in the field, through numerous techniques and strategies to address the impending challenges facing computer scientists today.The unique volume is useful for senior or graduate students in the information field and related tertiary specialities. It is also a suitable reference text for professionals, researchers, and academics in AI, machine learning, electrical & electronic engineering and biocomputing.
Introduction To Artificial Intelligence
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Author : Wolfgang Ertel
language : en
Publisher: Springer
Release Date : 2018-01-18
Introduction To Artificial Intelligence written by Wolfgang Ertel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-18 with Computers categories.
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.
Bayesian Reasoning And Machine Learning
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2012-02-02
Bayesian Reasoning And Machine Learning written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-02 with Computers categories.
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition
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Author : Andreas Miroslaus Wichert
language : en
Publisher: World Scientific
Release Date : 2020-07-08
Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition written by Andreas Miroslaus Wichert and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.
Bayesian Reasoning And Gaussian Processes For Machine Learning Applications
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Author : Hemachandran K
language : en
Publisher: CRC Press
Release Date : 2022-04-14
Bayesian Reasoning And Gaussian Processes For Machine Learning Applications written by Hemachandran K and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-14 with Business & Economics categories.
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.