Innovations In Bayesian Networks
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Innovations In Bayesian Networks
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Author : Dawn E. Holmes
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-02
Innovations In Bayesian Networks written by Dawn E. Holmes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-02 with Mathematics categories.
Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
Innovations In Bayesian Networks
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Author : Dawn E. Holmes
language : en
Publisher: Springer
Release Date : 2008-09-10
Innovations In Bayesian Networks written by Dawn E. Holmes and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-10 with Technology & Engineering categories.
Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
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.
Introduction To Bayesian Networks
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Author : Finn V. Jensen
language : en
Publisher: Springer
Release Date : 1997-08-15
Introduction To Bayesian Networks written by Finn V. Jensen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-15 with Mathematics categories.
Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.
Bayesian Networks And Bayesialab
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Author : Stefan Conrady
language : en
Publisher:
Release Date : 2015-07-01
Bayesian Networks And Bayesialab written by Stefan Conrady and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-01 with categories.
Innovative Approaches In Agent Based Modelling And Business Intelligence
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Author : Setsuya Kurahashi
language : en
Publisher: Springer
Release Date : 2018-12-08
Innovative Approaches In Agent Based Modelling And Business Intelligence written by Setsuya Kurahashi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-08 with Political Science categories.
This book thoroughly prepares intermediate-level readers for research in social science, organization studies, economics, finance, marketing science, and business science as complex adaptive systems. It presents the advantages of social simulation studies and business intelligence to those who are not familiar with the computational research approach, and offers experienced modelers various instructive examples of using agent-based modeling and business intelligence approaches to inspire their own work. In addition, the book discusses cutting-edge techniques for complex adaptive systems using their applications. To date, business science studies have focused only on data science and analyses of business problems. However, using these studies to enhance the capabilities of conventional techniques in the fields has not been investigated adequately. This book addresses managing the issues of societies, firms, and organizations to profit from interaction with agent-based modeling, human- and computer- mixed systems, and business intelligence approaches, an area that is fundamental for complex but bounded rational business environments. With detailed research by leading authors in the field, Innovative Approaches in Agent-Based Modelling and Business Intelligence inspires readers to join with other disciplines and extend the scope of the book with their own unique contributions. It also includes the common challenges encountered in computational social science and business science to enable researchers, students, and professionals to resolve their own problems.
Applications And Innovations In Intelligent Systems Xiv
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Author : Richard Ellis
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-05
Applications And Innovations In Intelligent Systems Xiv written by Richard Ellis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-05 with Computers categories.
The papers in this volume are the refereed application papers presented at AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2006. The papers present new and innovative developments in the field. The series serves as a key reference as to how AI technology has enabled organisations to solve complex problems and gain significant business benefit.
Development Of Clinical Decision Support Systems Using Bayesian Networks
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Author : Mario A. Cypko
language : en
Publisher: Springer Nature
Release Date : 2020-11-30
Development Of Clinical Decision Support Systems Using Bayesian Networks written by Mario A. Cypko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-30 with Computers categories.
For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert models of multidisciplinary clinical treatment decisions and solves challenges in their modeling. The presented methods, models and tools are developed in close and intensive cooperation between knowledge engineers and clinicians. In the course of this study, laryngeal cancer serves as an exemplary treatment decision. The reader is guided through a development process and new opportunities for research and development are opened up: in modeling and validation of workflows, guided modeling, semi-automated modeling, advanced Bayesian networks, model-user interaction, inter-institutional modeling and quality management.
Learning Bayesian Networks
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Author : Richard E. Neapolitan
language : en
Publisher: Prentice Hall
Release Date : 2004
Learning Bayesian Networks written by Richard E. Neapolitan and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Handbook On Neural Information Processing
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Author : Monica Bianchini
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-12
Handbook On Neural Information Processing written by Monica Bianchini and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-12 with Technology & Engineering categories.
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.