Advanced Methodologies For Bayesian Networks
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Advanced Methodologies For Bayesian Networks
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Author : Joe Suzuki
language : en
Publisher: Springer
Release Date : 2016-01-07
Advanced Methodologies For Bayesian Networks written by Joe Suzuki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-07 with Computers categories.
This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.
How To Fine Tune Bayesian Networks For Classification
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Author : Ionut B. Brandusoiu
language : en
Publisher: GAER Publishing House
Release Date : 2020-08-19
How To Fine Tune Bayesian Networks For Classification written by Ionut B. Brandusoiu and has been published by GAER Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-19 with Computers categories.
This book covers in the first part the theoretical aspects of Bayesian networks and their functionality, and then based on the discussed concepts it explains how to find-tune a Bayesian network to yield highly accurate prediction results which are adaptable to any classification tasks. The introductory part is extremely beneficial to someone new to learning Bayesian networks, while the more advanced notions are useful for everyone who wants to understand the mathematics behind Bayesian networks and how to find-tune them in order to generate the best predictive performance of a certain classification model.
Modeling And Reasoning With Bayesian Networks
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Author : Adnan Darwiche
language : en
Publisher: Cambridge University Press
Release Date : 2009-04-06
Modeling And Reasoning With Bayesian Networks written by Adnan Darwiche 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 2009-04-06 with Computers categories.
This book is 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 treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. 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.
Advanced Database Marketing
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Author : Koen W. De Bock
language : en
Publisher: Routledge
Release Date : 2016-03-23
Advanced Database Marketing written by Koen W. De Bock and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-23 with Business & Economics categories.
While the definition of database marketing hasn’t changed, its meaning has become more vivid, versatile and exciting than ever before. Advanced Database Marketing provides a state-of-the-art guide to the methods and applications that define this new era in database marketing, including advances in areas such as text mining, recommendation systems, internet marketing, and dynamic customer management. An impressive list of contributors including many of the thought-leaders in database marketing from across the world bring together chapters that combine the best academic research and business applications. The result is a definitive guide and reference for marketing and brand analysts, masters students, teachers and researchers in marketing analytics. The proliferation of marketing platforms and channels and the complexity of customer interactions create an urgent need for a multidisciplinary and analytical toolkit. Advanced Database Marketing is a resource to enable marketers to achieve insights and increased financial performance; to provide them with the capability to implement and evaluate approaches to marketing that will meet, in equal measure, the changing needs of customers and the businesses that serve them.
Probabilistic Modeling In Bioinformatics And Medical Informatics
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Author : Dirk Husmeier
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-06
Probabilistic Modeling In Bioinformatics And Medical Informatics written by Dirk Husmeier 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 2006-05-06 with Computers categories.
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
New Frontiers In Artificial Intelligence
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Author : Takashi Onoda
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-01-10
New Frontiers In Artificial Intelligence written by Takashi Onoda 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 2012-01-10 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of four workshops held as satellite events of the JSAI International Symposia on Artificial Intelligence 2010, in Tokyo, Japan, in November 2010. The 28 revised full papers with four papers for the following four workshops presented were carefully reviewed and selected from 70 papers. The papers are organized in sections Logic and Engineering of Natural Language Semantics (LENLS), Juris-Informatics (JURISIN), Advanced Methodologies for Bayesian Networks (AMBN), and Innovating Service Systems (ISS).
Knowledge Based Systems Advanced Concepts Techniques And Applications
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Author : Spyros Tzafestas
language : en
Publisher: World Scientific
Release Date : 1997-06-16
Knowledge Based Systems Advanced Concepts Techniques And Applications written by Spyros Tzafestas and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-06-16 with Computers categories.
The field of knowledge-based systems (KBS) has expanded enormously during the last years, and many important techniques and tools are currently available. Applications of KBS range from medicine to engineering and aerospace.This book provides a selected set of state-of-the-art contributions that present advanced techniques, tools and applications. These contributions have been prepared by a group of eminent researchers and professionals in the field.The theoretical topics covered include: knowledge acquisition, machine learning, genetic algorithms, knowledge management and processing under uncertainty, conflict detection and resolution, structured knowledge architectures, and natural language-based man-machine communication.The Applications include: Real-time decision support, system fault diagnosis, quality assessment, manufacturing production, robotic assembly, and robotic welding.The reader can save considerable time in searching the scattered literature in the field, and can find here a powerful set of how-to-do issues and results.
Computational Intelligence In Healthcare 4
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Author : Isabelle Bichindaritz
language : en
Publisher: Springer
Release Date : 2010-10-05
Computational Intelligence In Healthcare 4 written by Isabelle Bichindaritz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-05 with Technology & Engineering categories.
Computational Intelligence is comparatively a new field but it has made a tremendous progress in virtually every discipline right from engineering, science, business, m- agement, aviation to healthcare. Computational intelligence already has a solid track-record of applications to healthcare, of which this book is a continuation. We would like to refer the reader to the excellent previous volumes in this series on computational intelligence in heal- care [1-3]. This book is aimed at providing the most recent advances and state of the art in the practical applications of computational intelligence paradigms in healthcare. It - cludes nineteen chapters on using various computational intelligence methods in healthcare such as intelligent agents and case-based reasoning. A number of fielded applications and case studies are presented. Highlighted are in particular novel c- putational approaches to the semantic management of health information such as in the Web 2.0, mobile agents such as in portable devices, learning agents capable of adapting to diverse clinical settings through case-based reasoning, and statistical - proaches in computational intelligence. This book is targeted towards scientists, application engineers, professors, health professionals, professors, and students. Background information on computational intelligence has been provided whenever necessary to facilitate the comprehension of a broad audience including healthcare practitioners.
Probabilistic Graphical Models
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Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009-07-31
Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.
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.
An Introduction To Bayesian Inference Methods And Computation
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Author : Nick Heard
language : en
Publisher: Springer Nature
Release Date : 2021-10-17
An Introduction To Bayesian Inference Methods And Computation written by Nick Heard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-17 with Mathematics categories.
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.