Tag: Bayesian

Free Download Bayesian Filter Design for Computational Medicine: A State-Space Estimation Framework by Dilranjan S. Wickramasuriya, Rose T. Faghih
English | March 30, 2024 | ISBN: 3031471032 | 243 pages | MOBI | 51 Mb
This book serves as a tutorial that explains how different state estimators (Bayesian filters) can be built when all or part of the observations are binary. The book begins by briefly motivating the need for point process state estimation followed by an introduction to the overall approach, as well as some basic background material in statistics that are necessary for the equation derivations that are utilized in subsequent chapters. The subsequent chapters focus on different state-space models and provide step-by-step explanations on how to build the corresponding Bayesian filters.

Free Download Bayesian Modeling and Computation in Python (Chapman & Hall/CRC Texts in Statistical Science) by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao
English | December 29, 2021 | ISBN: 036789436X | 398 pages | MOBI | 29 Mb
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.

Free Download Maximum Entropy and Bayesian Methods: Cambridge, England, 1994 Proceedings of the Fourteenth International Workshop on Maximum Entropy and Bayesian Methods By E. J. Fordham, D. Xing, J. A. Derbyshire, S. J. Gibbs, T. A. Carpenter, L. D. Hall (auth.), John Skilling, Sibusiso Sibisi (eds.)
1996 | 323 Pages | ISBN: 9401065349 | PDF | 10 MB
This volume records papers given at the fourteenth international maximum entropy conference, held at St John’s College Cambridge, England. It seems hard to believe that just thirteen years have passed since the first in the series, held at the University of Wyoming in 1981, and six years have passed since the meeting last took place here in Cambridge. So much has happened. There are two major themes at these meetings, inference and physics. The inference work uses the confluence of Bayesian and maximum entropy ideas to develop and explore a wide range of scientific applications, mostly concerning data analysis in one form or another. The physics work uses maximum entropy ideas to explore the thermodynamic world of macroscopic phenomena. Of the two, physics has the deeper historical roots, and much of the inspiration behind the inference work derives from physics. Yet it is no accident that most of the papers at these meetings are on the inference side. To develop new physics, one must use one’s brains alone. To develop inference, computers are used as well, so that the stunning advances in computational power render the field open to rapid advance. Indeed, we have seen a revolution. In the larger world of statistics beyond the maximum entropy movement as such, there is now an explosion of work in Bayesian methods, as the inherent superiority of a defensible and consistent logical structure becomes increasingly apparent in practice.

Free Download Case Studies in Bayesian Statistics, Volume II By Emery N. Brown, Adam Sapirstein (auth.), Constantine Gatsonis, James S. Hodges, Robert E. Kass, Nozer D. Singpurwalla (eds.)
1995 | 370 Pages | ISBN: 0387945660 | PDF | 27 MB
Like its predecessor, this second volume presents detailed applications of Bayesian statistical analysis, each of which emphasizes the scientific context of the problems it attempts to solve. The emphasis of this volume is on biomedical applications. These papers were presented at a workshop at Carnegie-Mellon University in 1993.

Free Download Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) by Marco Scutari, Jean-Baptiste Denis
English | July 29, 2021 | ISBN: 0367366517 | 274 pages | MOBI | 19 Mb
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.

Free Download Lori A. Dalton, Edward R. Dougherty, "Optimal Bayesian Classification"
English | 2020 | pages: 360 | ISBN: 1510630694 | PDF | 4,2 mb
The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. For classification, the random process is a feature-label distribution, and a Bayes classifier minimizes classification error. Rarely do we know the feature-label distribution or have sufficient data to estimate it. To best use available knowledge and data, this book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. The origins of this approach lie in estimating classifier error when there are insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and find an optimal classifier relative to the posterior distribution over the uncertainty class this being an optimal Bayesian classifier.

Free Download Bayesian Analysis with Python – Third Edition: A practical guide to probabilistic modeling by Osvaldo Martin, Christopher Fonnesbeck, Thomas Wiecki
English | January 31, 2024 | ISBN: 1805127160 | 394 pages | PDF, EPUB | 74 Mb
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features

Free Download Bayesian Methods and Ethics in a Clinical Trial Design By
1996 | 322 Pages | ISBN: 0471846805 | PDF | 6 MB
How to conduct clinical trials in an ethical and scientifically responsible manner This book presents a methodology for clinical trials that produces improved health outcomes for patients while obtaining sound and unambiguous scientific data. It centers around a real-world test case–involving a treatment for hypertension after open heart surgery–and explains how to use Bayesian methods to accommodate both ethical and scientific imperatives. The book grew out of the direct involvement in the project by a diverse group of experts in medicine, statistics, philosophy, and the law. Not only do they contribute essays on the scientific, technological, legal, and ethical aspects of clinical trials, but they also critique and debate each other’s opinions, creating an interesting, personalized text. Bayesian Methods and Ethics in a Clinical Trial Design * Answers commonly raised questions about Bayesian methods * Describes the advantages and disadvantages of this method compared with other methods * Applies current ethical theory to a particular class of design for clinical trials * Discusses issues of informed consent and how to serve a patient’s best interest while still obtaining uncontaminated scientific data * Shows how to use Bayesian probabilistic methods to create computer models from elicited prior opinions of medical experts on the best treatment for a type of patient * Contains several chapters on the process, results, and computational aspects of the test case in question * Explores American law and the legal ramifications of using human subjects For statisticians and biostatisticians, and for anyone involved with medicine and public health, this book provides both a practical guide and a unique perspective on the connection between technological developments, human factors, and some of the larger ethical issues of our times.Content: Chapter 1 Introduction (pages 1-18): Joseph B. KadaneChapter 2 Ethically Optimizing Clinical Trials (pages 19-63): Kenneth F. SchaffnerChapter 3 Admissibility of Treatments (pages 65-113): Nell SedranskChapter 4 Statistical Issues in the Analysis of Data Gathered in the New Designs (pages 115-125): Joseph B. Kadane and Teddy SeidenfeldChapter 5 Introduction to the Verapamil/Nitroprusside Study (pages 127-130): Joseph B. KadaneChapter 6 The Mechanics of Conducting a Clinical Trial (pages 131-143): Eugenie S. Heitmiller and Thomas J. J. BlanckChapter 7 The Verapamil/Nitroprusside Study: Comments on "The Mechanics of Conducting a Clinical Trial" (pages 145-150): John L. CoulehanChapter 8 Computational Aspects of the Verapamil/Nitroprusside Study (pages 151-158): Lionel A. GalwayChapter 9 Being an Expert (pages 159-162): Thomas J. J. Blanck, Thomas J. Conahan, Robert G. Merin, Richard L. Prager and James J. RichterChapter 10 Issues of Statistical Design (pages 163-170): Nell SedranskChapter 11 Operational History and Procedural Feasibility (pages 171-175): Joseph B. KadaneChapter 12 Verapamil versus Nitroprusside: Results of the Clinical Trial I (pages 177-210): Joseph B. Kadane and Nell SedranskChapter 13 Verapamil versus Nitroprusside: Results of the Clinical Trial II (pages 211-219): Eugenie S. Heitmiller, Joseph B. Kadane, Nell Sedransk and Thomas J. J. BlanckChapter 14 The Law of Clinical Testing with Human Subjects: Legal Implications of the New and Existing Methodologies (pages 221-249): David KairysChapter 15 Commentary I on "The Law of Clinical Testing with Human Subjects" (pages 251-255): Dale Moore and A. John PoppChapter 16 Commentary II on "The Law of Clinical Testing with Human Subjects" (pages 257-261): Katheryn D. KatzChapter 17 Author’s Response to Commentaries I and II (pages 263-266): David KairysChapter 18 Whether to Participate in a Clinical Trial: The Patient’s View (pages 267-305): Lawrence J. Emrich and Nell SedranskChapter 19 Epilogue (pages 307-310): Joseph B. Kadane

Free Download Bayesian Statistics and Marketing, 2nd Edition by Peter E. Rossi, Greg M. Allenby, Sanjog Misra
English | July 15th, 2024 | ISBN: 1394219113 | 402 pages | True PDF | 8.85 MB
Fine-tune your marketing research with this cutting-edge statistical toolkit

Free Download Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress By Riccardo Rebonato
2010 | 238 Pages | ISBN: 0470666013 | PDF | 2 MB
In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit.Based on the author’s extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches.The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.