Machine Learning And Probabilistic Graphical Models For Decision Support Systems

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Machine Learning and Probabilistic Graphical Models for Decision Support Systems

Author : Kim Phuc Tran
Publisher : CRC Press
ISBN 13 : 100077144X
Total Pages : 330 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Machine Learning and Probabilistic Graphical Models for Decision Support Systems by : Kim Phuc Tran

Download or read book Machine Learning and Probabilistic Graphical Models for Decision Support Systems written by Kim Phuc Tran and published by CRC Press. This book was released on 2022-10-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.

Probabilistic Graphical Models

Author : Daphne Koller
Publisher : MIT Press
ISBN 13 : 0262258358
Total Pages : 1270 pages
Book Rating : 4.2/5 (622 download)

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Book Synopsis Probabilistic Graphical Models by : Daphne Koller

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 1270 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.

Bayesian Networks and Decision Graphs

Author : Thomas Dyhre Nielsen
Publisher : Springer Science & Business Media
ISBN 13 : 0387682821
Total Pages : 457 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Bayesian Networks and Decision Graphs by : Thomas Dyhre Nielsen

Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2009-03-17 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.

Probabilistic Graphical Models

Author : Luis Enrique Sucar
Publisher : Springer
ISBN 13 : 144716699X
Total Pages : 267 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Probabilistic Graphical Models by : Luis Enrique Sucar

Download or read book Probabilistic Graphical Models written by Luis Enrique Sucar and published by Springer. This book was released on 2015-06-19 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.

Modeling and Simulating Complex Business Perceptions

Author : Zoumpolia Dikopoulou
Publisher : Springer Nature
ISBN 13 : 3030814963
Total Pages : 174 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Modeling and Simulating Complex Business Perceptions by : Zoumpolia Dikopoulou

Download or read book Modeling and Simulating Complex Business Perceptions written by Zoumpolia Dikopoulou and published by Springer Nature. This book was released on 2021-11-06 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy cognitive maps (FCMs) have gained popularity in the scientific community due to their capabilities in modeling and decision making for complex problems.This book presents a novel algorithm called glassoFCM to enable automatic learning of FCM models from data. Specifically, glassoFCM is a combination of two methods, glasso (a technique originated from machine learning) for data modeling and FCM simulation for decision making. The book outlines that glassoFCM elaborates simple, accurate, and more stable models that are easy to interpret and offer meaningful decisions. The research results presented are based on an investigation related to a real-world business intelligence problem to evaluate characteristics that influence employee work readiness.Finally, this book provides readers with a step-by-step guide of the 'fcm' package to execute and visualize their policies and decisions through the FCM simulation process.

Probabilistic Reasoning in Multiagent Systems

Author : Yang Xiang
Publisher : Cambridge University Press
ISBN 13 : 1139434462
Total Pages : 310 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Probabilistic Reasoning in Multiagent Systems by : Yang Xiang

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.

Learning in Graphical Models

Author : M.I. Jordan
Publisher : Springer Science & Business Media
ISBN 13 : 9401150141
Total Pages : 658 pages
Book Rating : 4.4/5 (11 download)

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Book Synopsis Learning in Graphical Models by : M.I. Jordan

Download or read book Learning in Graphical Models written by M.I. Jordan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.

Decision Making Under Uncertainty

Author : Mykel J. Kochenderfer
Publisher : MIT Press
ISBN 13 : 0262331713
Total Pages : 350 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Bayesian Reasoning and Machine Learning

Author : David Barber
Publisher : Cambridge University Press
ISBN 13 : 0521518148
Total Pages : 739 pages
Book Rating : 4.5/5 (215 download)

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Book Synopsis Bayesian Reasoning and Machine Learning by : David Barber

Download or read book Bayesian Reasoning and Machine Learning written by David Barber and published by Cambridge University Press. This book was released on 2012-02-02 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Machine learning using approximate inference

Author : Christian Andersson Naesseth
Publisher : Linköping University Electronic Press
ISBN 13 : 9176851613
Total Pages : 39 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis Machine learning using approximate inference by : Christian Andersson Naesseth

Download or read book Machine learning using approximate inference written by Christian Andersson Naesseth and published by Linköping University Electronic Press. This book was released on 2018-11-27 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models. There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them. First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.

Computational Intelligence in Data Science

Author : Mieczyslaw Lech Owoc
Publisher : Springer Nature
ISBN 13 : 3031699866
Total Pages : 532 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Computational Intelligence in Data Science by : Mieczyslaw Lech Owoc

Download or read book Computational Intelligence in Data Science written by Mieczyslaw Lech Owoc and published by Springer Nature. This book was released on with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Intelligence for Smart Manufacturing

Author : Kim Phuc Tran
Publisher : Springer Nature
ISBN 13 : 3031305108
Total Pages : 271 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Artificial Intelligence for Smart Manufacturing by : Kim Phuc Tran

Download or read book Artificial Intelligence for Smart Manufacturing written by Kim Phuc Tran and published by Springer Nature. This book was released on 2023-06-01 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI). As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical application of these tools in real-world scenarios. Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challenges and opportunities presented by Industry 5.0.

MEDINFO 2015: EHealth-enabled Health

Author : I.N. Sarkar
Publisher : IOS Press
ISBN 13 : 1614995648
Total Pages : 1180 pages
Book Rating : 4.6/5 (149 download)

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Book Synopsis MEDINFO 2015: EHealth-enabled Health by : I.N. Sarkar

Download or read book MEDINFO 2015: EHealth-enabled Health written by I.N. Sarkar and published by IOS Press. This book was released on 2015-08-12 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health and Biomedical Informatics is a rapidly evolving multidisciplinary field; one in which new developments may prove crucial in meeting the challenge of providing cost-effective, patient-centered healthcare worldwide. This book presents the proceedings of MEDINFO 2015, held in São Paulo, Brazil, in August 2015. The theme of this conference is ‘eHealth-enabled Health’, and the broad spectrum of topics covered ranges from emerging methodologies to successful implementations of innovative applications, integration and evaluation of eHealth systems and solutions. Included here are 178 full papers and 248 poster abstracts, selected after a rigorous review process from nearly 800 submissions by 2,500 authors from 59 countries. The conference brings together researchers, clinicians, technologists and managers from all over the world to share their experiences on the use of information methods, systems and technologies to promote patient-centered care, improving patient safety, enhancing care outcomes, facilitating translational research and enabling precision medicine, as well as advancing education and skills in Health and Biomedical Informatics. This comprehensive overview of Health and Biomedical Informatics will be of interest to all those involved in designing, commissioning and providing healthcare, wherever they may be.

Cybersecurity and Data Management Innovations for Revolutionizing Healthcare

Author : Murugan, Thangavel
Publisher : IGI Global
ISBN 13 :
Total Pages : 402 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Cybersecurity and Data Management Innovations for Revolutionizing Healthcare by : Murugan, Thangavel

Download or read book Cybersecurity and Data Management Innovations for Revolutionizing Healthcare written by Murugan, Thangavel and published by IGI Global. This book was released on 2024-07-23 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: In todays digital age, the healthcare industry is undergoing a paradigm shift towards embracing innovative technologies to enhance patient care, improve efficiency, and ensure data security. With the increasing adoption of electronic health records, telemedicine, and AI-driven diagnostics, robust cybersecurity measures and advanced data management strategies have become paramount. Protecting sensitive patient information from cyber threats is critical and maintaining effective data management practices is essential for ensuring the integrity, accuracy, and availability of vast amounts of healthcare data. Cybersecurity and Data Management Innovations for Revolutionizing Healthcare delves into the intersection of healthcare, data management, cybersecurity, and emerging technologies. It brings together a collection of insightful chapters that explore the transformative potential of these innovations in revolutionizing healthcare practices around the globe. Covering topics such as advanced analytics, data breach detection, and privacy preservation, this book is an essential resource for healthcare professionals, researchers, academicians, healthcare professionals, data scientists, cybersecurity experts, and more.

Intelligent Systems for Sustainable Person-Centered Healthcare

Author : Dalia Kriksciuniene
Publisher : Springer Nature
ISBN 13 : 3030793532
Total Pages : 256 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Intelligent Systems for Sustainable Person-Centered Healthcare by : Dalia Kriksciuniene

Download or read book Intelligent Systems for Sustainable Person-Centered Healthcare written by Dalia Kriksciuniene and published by Springer Nature. This book was released on 2022 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Person-Centered Care (PCC) conceptual background of healthcare positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based concep- tual background triggers enhanced application of Artificial Intelligence (AI), as it dissolves the limits of processing traditional medical data records. The ambition of taking care of a person health by knowing life conditions, values, and expectations for nurturing own health adds new dimensions for making PCC operational.

Innovations in Machine Learning and IoT for Water Management

Author : Kumar, Abhishek
Publisher : IGI Global
ISBN 13 :
Total Pages : 331 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Innovations in Machine Learning and IoT for Water Management by : Kumar, Abhishek

Download or read book Innovations in Machine Learning and IoT for Water Management written by Kumar, Abhishek and published by IGI Global. This book was released on 2023-11-27 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Water, our planet's life force, faces multiple challenges in the 21st century, including surging global demand, shifting climate patterns, and the urgent need for sustainable management. Guidance, knowledge, and hope is sharply needed in academia and technology industries, and Innovations in Machine Learning and IoT for Water Management is a formidable resource to provide these necessities. This book delves into the dynamic synergy of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), ushering in a new era of water resource stewardship. This book embarks on a journey through the frontiers of AI and IoT, unveiling their transformative impact on water management. From the vantage point of satellite imagery analysis, it scrutinizes the Earth's vital signs, unlocking crucial insights into water resources. It chronicles the rise of AI-powered predictive analytics, a revolutionary force propelling precision water usage and conservation. This book explains how IoT can be an effective tool to increase intelligence of our water systems. The book meticulously navigates through domains as diverse as aquifer monitoring, hydropower generation optimization, and predictive analytics for water consumption. This book caters to a diverse audience, from water management experts and environmental scientists to data science aficionados and IoT enthusiasts. Engineers seeking to reimagine the future of water systems, technology enthusiasts eager to delve into AI's potential, and individuals impassioned by preserving water will all find a well-needed resource in these pages.

Hybrid Random Fields

Author : Antonino Freno
Publisher : Springer Science & Business Media
ISBN 13 : 3642203086
Total Pages : 217 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Hybrid Random Fields by : Antonino Freno

Download or read book Hybrid Random Fields written by Antonino Freno and published by Springer Science & Business Media. This book was released on 2011-04-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.