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Bayesian Belief Networks
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Book Synopsis Bayesian Networks by : Marco Scutari
Download or read book Bayesian Networks written by Marco Scutari and published by CRC Press. This book was released on 2021-07-28 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the material step-by-step starting from meaningful examples Steps detailed with R code in the spirit of reproducible research Real world data analyses from a Science paper reproduced and explained in detail Examples span a variety of fields across social and life sciences Overview of available software in and outside R
Book Synopsis Bayesian Networks by : Olivier Pourret
Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Bayesian Networks by : Richard E. Neapolitan
Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Probability for Machine Learning by : Jason Brownlee
Download or read book Probability for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-09-24 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
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.
Book Synopsis Modeling and Reasoning with Bayesian Networks by : Adnan Darwiche
Download or read book Modeling and Reasoning with Bayesian Networks written by Adnan Darwiche and published by Cambridge University Press. This book was released on 2009-04-06 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides 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 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.
Download or read book Bayesian Networks written by Timo Koski and published by Wiley. This book was released on 2009-09-24 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.
Book Synopsis Bayesian Networks Handbook by : Mick Benson
Download or read book Bayesian Networks Handbook written by Mick Benson and published by . This book was released on 2015-02-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective tools to incorporate different information sources with varying levels of uncertainty in a mathematically secure and calculatively effective way. A Bayesian network is a graphical model that ciphers probabilistic relationships among variables of interest. This graphical paradigm has a few significant advantages: firstly, due to the dependencies among all the variables, missing nodes data is also compensated; secondly, belief network sets up the simple relationships and it is easier to identify problematic areas and consequences; thirdly, it has both casual and probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data. This book analytically and comprehensively describes various aspects of Bayesian networks which will be of great help to students, researchers and professionals in various fields which utilize applications of this model system.
Book Synopsis Bayesian Belief Networks by : Remco Ronaldus Bouckaert
Download or read book Bayesian Belief Networks written by Remco Ronaldus Bouckaert and published by . This book was released on 1995 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Innovations in Bayesian Networks by : Dawn E. Holmes
Download or read book Innovations in Bayesian Networks written by Dawn E. Holmes and published by Springer Science & Business Media. This book was released on 2008-10-02 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Interactive Collaborative Information Systems by : Robert Babuška
Download or read book Interactive Collaborative Information Systems written by Robert Babuška and published by Springer. This book was released on 2010-03-22 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.
Book Synopsis Bayesian Networks by : Marco Scutari
Download or read book Bayesian Networks written by Marco Scutari and published by CRC Press. This book was released on 2014-06-20 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.
Book Synopsis Probabilistic Reasoning and Bayesian Belief Networks by : Alexander Gammerman
Download or read book Probabilistic Reasoning and Bayesian Belief Networks written by Alexander Gammerman and published by . This book was released on 1995 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Networks by : Wichian Premchaiswadi
Download or read book Bayesian Networks written by Wichian Premchaiswadi and published by BoD – Books on Demand. This book was released on 2012-04-20 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. First, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Second, a Bayesian network can be used to learn causal relationships, and hence can be used to gain an understanding about a problem domain and to predict the consequences of intervention. Third, because the model has both causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in a causal form) and data. Fourth, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach to avoid the over fitting of data.
Book Synopsis Advances in Artificial Intelligence by : Eleni Stroulia
Download or read book Advances in Artificial Intelligence written by Eleni Stroulia and published by Springer. This book was released on 2001-05-16 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI 2001 is the 14th in the series of Arti cial Intelligence conferences sponsored by the Canadian Society for Computational Studies of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As was the case last year too, the conference is being held in conjunction with the annual conferences of two other Canadian societies, Graphics Interface (GI 2001) and Vision Int- face (VI 2001). We believe that the overall experience will be enriched by this conjunction of conferences. This year is the \silver anniversary" of the conference: the rst Canadian AI conference was held in 1976 at UBC. During its lifetime, it has attracted Canadian and international papers of high quality from a variety of AI research areas. All papers submitted to the conference received at least three indep- dent reviews. Approximately one third were accepted for plenary presentation at the conference. The best paper of the conference will be invited to appear in Computational Intelligence.
Book Synopsis Developments in Environmental Modelling by : S.E. Jorgensen
Download or read book Developments in Environmental Modelling written by S.E. Jorgensen and published by Elsevier. This book was released on 2016-04-20 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of models to assess water quality is becoming increasingly important worldwide. In order to be able to develop a good model, it is necessary to have a good quantitative and ecological description of physical, chemical and biological processes in ecosystems. Such descriptions may be called ``submodels''. This book presents the most important, but not all, submodels applied in water quality modelling. Each chapter deals with a specific physical process and covers its importance, the most applicable submodels (and how to select one), parameter values and their determination, and future research needs.The book will be an excellent reference source for environmental engineers, ecological modellers and all those interested in the modelling of water quality systems.
Book Synopsis Strategic Economic Decision-Making by : Jeff Grover
Download or read book Strategic Economic Decision-Making written by Jeff Grover and published by Springer Science & Business Media. This book was released on 2012-12-05 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study. Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.
