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Author : Samuel R. Houston,William L. Duff,Robert M. Lynch
Publisher : Unknown
Page : 84 pages
File Size : 47,5 Mb
Release : 1975
Category : Mathematics
ISBN : UOM:35128000148229
Author : Ming-Hui Chen,Peter Müller,Dongchu Sun,Keying Ye,Dipak K. Dey
Publisher : Springer Science & Business Media
Page : 631 pages
File Size : 52,8 Mb
Release : 2010-07-24
Category : Mathematics
ISBN : 9781441969446
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author : Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publisher : CRC Press
Page : 165 pages
File Size : 47,7 Mb
Release : 2022-04-14
Category : Business & Economics
ISBN : 9781000569599
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Author : Derek W. Bunn
Publisher : Birkhäuser
Page : 258 pages
File Size : 50,8 Mb
Release : 1978
Category : Juvenile Nonfiction
ISBN : STANFORD:36105035772008
Thls collectlon of papers owes Its orlgin to arecent conference on the toplc of dec ision analysis organised by the Royal Economlc Socfety, durlng the per Iod when one of us {H.T.} served as Programme Secretary for the Socfety. The papers Included here were selected especially for the contrfbutfon they made to the fmplementatfon of decislon analytlc methods In the field of pol fcy formulation. These selected con ference papers have furthermore been supplemented by several fnvited contrlbutions In order to provide a more complete exposition of the overall theme. Thus, the volume now contalns a set of original papers whlch we bel feve contribute slgnlficantly to the most Important aspects of this toplc. The work is grouped into two parts. Part I contalns a critique of analytical meth ods In pollcy formulatlon and defines the essential characterlstlcs of the pollcy process. Although we advocate the declslon analysis approach Insofar as It provfdes what we consfder to be the most acceptable paradfgm for rational action, we do so' onlyon the balance of its merfts, based upon our personal experience of consulting work In the Decislon Analysis Unft at the London Business School. The papars Inclu ded In thfs first part represent, therefore, a dlspassionate evaluation of the place of analytical methods in the effectlve formulation of pol fcy. Part 2 looks at some of the more important aspects In the Implementation of the decisfon analysis approach.
Author : Olivier Pourret,Patrick Naïm,Bruce Marcot
Publisher : John Wiley & Sons
Page : 446 pages
File Size : 50,7 Mb
Release : 2008-04-30
Category : Mathematics
ISBN : 0470994541
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.
Author : Jim Q. Smith
Publisher : Cambridge University Press
Page : 349 pages
File Size : 49,5 Mb
Release : 2010-09-23
Category : Mathematics
ISBN : 9781139491112
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.
Author : Mykel J. Kochenderfer
Publisher : MIT Press
Page : 350 pages
File Size : 50,5 Mb
Release : 2015-07-24
Category : Computers
ISBN : 9780262331715
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.
Author : Fouad Sabry
Publisher : One Billion Knowledgeable
Page : 98 pages
File Size : 43,6 Mb
Release : 2023-06-27
Category : Computers
ISBN : PKEY:6610000470914
What Is Decision Analysis The term "decision analysis" (DA) refers to the academic field that encompasses the theory, technique, and professional practice that are required to tackle significant decisions in an organized fashion. It is possible to prescribe a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision. Additionally, decision analysis includes many procedures, methods, and tools for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, as well as for other corporate and non-corporate stakeholders. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Decision Analysis Chapter 2: Decision Theory Chapter 3: Multiple-criteria Decision Analysis Chapter 4: Expected Value of Sample Information Chapter 5: Decision-making Software Chapter 6: Robust Decision-making Chapter 7: Expected Value of Including Uncertainty Chapter 8: Decision Quality Chapter 9: Value Tree Analysis Chapter 10: Bayesian Inference in Marketing (II) Answering the public top questions about decision analysis. (III) Real world examples for the usage of decision analysis in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of decision analysis' 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 decision analysis.
Author : Norman Fenton,Martin Neil
Publisher : CRC Press
Page : 527 pages
File Size : 41,8 Mb
Release : 2012-11-07
Category : Business & Economics
ISBN : 9781439809105
Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
Author : James O. Berger
Publisher : Springer Science & Business Media
Page : 648 pages
File Size : 53,5 Mb
Release : 1985-08-21
Category : Business & Economics
ISBN : 0387960988
"The outstanding strengths of the book are its topic coverage, references, exposition, examples and problem sets... This book is an excellent addition to any mathematical statistician's library." -Bulletin of the American Mathematical Society In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Author : Sucar, L. Enrique
Publisher : IGI Global
Page : 444 pages
File Size : 48,9 Mb
Release : 2011-10-31
Category : Computers
ISBN : 9781609601676
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.
Author : Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publisher : CRC Press
Page : 147 pages
File Size : 53,8 Mb
Release : 2022-04-14
Category : Business & Economics
ISBN : 9781000569582
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Author : Giovanni Parmigiani,Lurdes Inoue
Publisher : John Wiley & Sons
Page : 402 pages
File Size : 48,9 Mb
Release : 2009-04-15
Category : Mathematics
ISBN : 9780470746677
Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice. The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives. This book: Provides a rich collection of techniques and procedures. Discusses the foundational aspects and modern day practice. Links foundations to practical applications in biostatistics, computer science, engineering and economics. Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics. Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.
Author : Ming-Hui Chen,Peter Müller,Dongchu Sun,Keying Ye,Dipak Dey
Publisher : Springer
Page : 631 pages
File Size : 40,6 Mb
Release : 2010-08-05
Category : Mathematics
ISBN : 1441969454
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author : Giovanni Parmigiani
Publisher : John Wiley & Sons
Page : 288 pages
File Size : 47,6 Mb
Release : 2002-03
Category : Mathematics
ISBN : UOM:39015055836467
Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making. * Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory. * Driven by three real applications, presented as extensively detailed case studies. * Case studies include simplified versions of the analysis, to approach complex modelling in stages. * Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling. * Accessible to readers with only a basic statistical knowledge. Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health service research and health policy.