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Convex Bayes Decision Networks
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Download or read book Convex Bayes Decision Networks written by and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This final report describes the results of a project to study the use of Bayesian networks to implement Levi's epistemic utility decision theory. The results of this project can be roughly categorized as falling into one of three areas: (1) implementation of Levi's theory using Bayesian networks, (2) development of Bayesian network updating algorithms for continuous valued network nodes, and (3) application of continuous Bayesian networks to stochastic filtering problems. We found that although Bayesian networks do not preserve the convexity of sets of distributions, Levi's decision theory can still be implemented by computing extremely points in these sets. Also, we developed a method of implementing Bayesian networks containing continuous valued nodes using Gaussian sum approximations; this method is applicable in any context in which a Bayesian network may be applied and, in particular, is not restricted to networks used to implement Levi's theory. Finally we investigated the application of Bayesian networks to stochastic filtering problems and demonstrated this application through a simple angle-only target tracking problem. This report provides an overview of these results, which are fully documented in the PhD dissertation and papers referenced in Section 3.
Book Synopsis Decision Making Based on Convex Sets of Probability Distributions by : Fabio Cozman
Download or read book Decision Making Based on Convex Sets of Probability Distributions written by Fabio Cozman and published by . This book was released on 1997 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "The thesis advanced by this dissertation is that convex sets of probability distributions provide a powerful representational framework for decision making activities in Robotics and Artificial Intelligence. The primary contribution of this dissertation is the development of algorithms for inference and estimation in two domains. The first domain is robustness analysis for graphical models of inference. Novel results are developed for models that represent perturbations in Bayesian networks by convex sets of probability distributions. The dissertation reports on a system, called JavaBayes, that uniformly handles standard probability distributions and convex sets of probability distributions. This system is publicly available and has been used for teaching and research throughout the world. The second domain explored in this dissertation is outdoor visual position estimation for mobile robots. A novel algorithm for visual position estimation is derived in the context of remote driving for mobile robots in open, natural environments. This algorithm has been implemented in the Viper system, and field tested in a variety of environments, displaying accuracy and functionality levels that surpass previous work."
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 Bayesian Decision Networks by : Fouad Sabry
Download or read book Bayesian Decision Networks written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-01 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Bayesian Decision Networks A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bayesian network Chapter 2: Influence diagram Chapter 3: Graphical model Chapter 4: Hidden Markov model Chapter 5: Decision tree Chapter 6: Gibbs sampling Chapter 7: Decision analysis Chapter 8: Value of information Chapter 9: Probabilistic forecasting Chapter 10: Causal graph (II) Answering the public top questions about bayesian decision networks. (III) Real world examples for the usage of bayesian decision networks in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian decision networks' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian decision networks.
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 Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis by : Uffe B. Kjærulff
Download or read book Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis written by Uffe B. Kjærulff and published by Springer Science & Business Media. This book was released on 2007-12-20 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.
Book Synopsis Bayesian Networks and Decision Graphs by :
Download or read book Bayesian Networks and Decision Graphs written by and published by . This book was released on 2001 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and to understand them, and when communicated to a computer, they can easily be compiled. Furthermore, handy algorithms are developed for analyses of the models and for providing responses to a wide range of requests such as belief updating, determining optimal strategies, conflict analyses of evidence, and most probable explanation. The book emphasizes both the human and the computer sides. Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics. Part II is devoted to the presentation of algorithms and complexity issues. This part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language. The author also: - provides a well-founded practical introduction to Bayesian networks, decision trees and influence diagrams; - gives several examples and exercises exploiting the computer systems for Bayesian netowrks and influence diagrams; - gives practical advice on constructiong Bayesian networks and influence diagrams from domain knowledge; - embeds decision making into the framework of Bayesian networks; - presents in detail the currently most efficient algorithms for probability updating in Bayesian networks; - discusses a wide range of analyes tools and model requests together with algorithms for calculation of responses; - gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams.
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 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.
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 2007-06-06 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.
Download or read book Bayesian Networks written by Timo Koski and published by John Wiley & Sons. This book was released on 2011-08-26 with total page 275 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 Decision Analysis by : Jim Q. Smith
Download or read book Bayesian Decision Analysis written by Jim Q. Smith and published by Cambridge University Press. This book was released on 2010-09-23 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.
Book Synopsis Probabilistic Networks and Expert Systems by : Robert G. Cowell
Download or read book Probabilistic Networks and Expert Systems written by Robert G. Cowell and published by Springer Science & Business Media. This book was released on 2007-07-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.
Book Synopsis Benefits of Bayesian Network Models by : Philippe Weber
Download or read book Benefits of Bayesian Network Models written by Philippe Weber and published by John Wiley & Sons. This book was released on 2016-08-23 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.
Book Synopsis Machine Learning by : Sergios Theodoridis
Download or read book Machine Learning written by Sergios Theodoridis and published by Academic Press. This book was released on 2020-02-19 with total page 1162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: - Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). - Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. - Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method - Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling - Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more
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.
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
