Bayesian Statistics 7
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Bayesian Statistics 7
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Author : J. M. Bernardo
language : en
Publisher: Oxford University Press
Release Date : 2003-07-03
Bayesian Statistics 7 written by J. M. Bernardo and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-03 with Mathematics categories.
This volume contains the proceedings of the 7th Valencia International Meeting on Bayesian Statistics. This conference is held every four years and provides the main forum for researchers in the area of Bayesian statistics to come together to present and discuss frontier developments in the field.
Bayesian Methods In Reliability
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Author : P. Sander
language : en
Publisher: Springer Science & Business Media
Release Date : 1991
Bayesian Methods In Reliability written by P. Sander 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 1991 with Mathematics categories.
1. Introduction to Bayesian Methods in Reliability.- 1. Why Bayesian Methods?.- 1.1 Sparse data.- 1.2 Decision problems.- 2. Bayes' Theorem.- 3. Examples from a Safety Study on Gas transmission Pipelines.- 3.1 Estimating the probability of the development of a big hole.- 3.2 Estimating the leak rate of a gas transmission pipeline.- 4. Conclusions.- References.- 2. An Overview of the Bayesian Approach.- 1. Background.- 2. Probability Concepts.- 3. Notation.- 4. Reliability Concepts and Models.- 5. Forms of Data.- 6. Statistical Problems.- 7. Review of Non-Bayesian Statistical Methods.- 8. Desiderata for Decision-Oriented Statistical Methodology.- 9. Decision-Making.- 10. Degrees of Belief as Probabilities.- 11. Bayesian Statistical Philosophy.- 12. A Simple Illustration of Bayesian Learning.- 13. Bayesian Approaches to Typical Statistical Questions.- 14. Assessment of Prior Densities.- 15. Bayesian Inference for some Univariate Probability Models.- 16. Approximate Analysis under Great Prior Uncertainty.- 17. Problems Involving many Parameters: Empirical Bayes.- 18. Numerical Methods for Practical Bayesian Statistics.- References.- 3. Reliability Modelling and Estimation.- 1. Non-Repairable Systems.- 1.1 Introduction.- 1.2 Describing reliability.- 1.3 Failure time distributions.- 2. Estimation.- 2.1 Introduction.- 2.2 Classical methods.- 2.3 Bayesian methods.- 3. Reliability estimation.- 3.1 Introduction.- 3.2 Binomial sampling.- 3.3 Pascal sampling.- 3.4 Poisson sampling.- 3.5 Hazard rate estimation.- References.- 4. Repairable Systems and Growth Models.- 1. Introduction.- 2. Good as New: the Renewal Process.- 3. Estimation.- 4. The Poisson Process.- 5. Bad as old: the Non-Homogeneous Poisson Process.- 6. Classical Estimation.- 7. Exploratory Analysis.- 8. The Duane Model.- 9. Bayesian Analysis.- References.- 5. The Use of Expert Judgement in Risk Assessment.- 1. Introduction.- 2. Independence Preservation.- 3. The Quality of Experts' Judgement.- 4. Calibration Sets and Seed Variables.- 5. A Classical Model.- 6. Bayesian Models.- 7. Some Experimental Results.- References.- 6. Forecasting Software Reliability.- 1. Introduction.- 2. The Software Reliability Growth Problem.- 3. Some Software Reliability Growth Models.- 3.1 Jelinski and Moranda (JM).- 3.2 Bayesian Jelinski-Moranda (BJM).- 3.3 Littlewood (L).- 3.4 Littlewood and Verrall (LV).- 3.5 Keiller and Littlewood (KL).- 3.6 Weibull order statistics (W).- 3.7 Duane (D).- 3.8 Goel-Okumoto (GO).- 3.9 Littlewood NHPP (LNHPP).- 4. Examples of Use.- 5. Analysis of Predictive Quality.- 5.1 The u-plot.- 5.2 The y-plot, and scatter plot of u's.- 5.3 Measures of 'noise'.- 5.3.1 Braun statistic.- 5.3.2 Median variability.- 5.3.3 Rate variability.- 5.4 Prequential likelihood.- 6. Examples of Predictive Analysis.- 7. Adapting and Combining Predictions; Future Directions.- 8 Summary and Conclusions.- Acknowledgements.- References.- References.- Author index.
Bayesian Statistics The Fun Way
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Author : Will Kurt
language : en
Publisher: No Starch Press
Release Date : 2019-07-09
Bayesian Statistics The Fun Way written by Will Kurt and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-09 with Mathematics categories.
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.
Bayesian Statistics For Beginners
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Author : Therese M. Donovan
language : en
Publisher: Oxford University Press, USA
Release Date : 2019
Bayesian Statistics For Beginners written by Therese M. Donovan and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Mathematics categories.
This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
Introduction To Bayesian Statistics
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Author : William M. Bolstad
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-02
Introduction To Bayesian Statistics written by William M. Bolstad and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-02 with Mathematics categories.
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
Bayesian Core A Practical Approach To Computational Bayesian Statistics
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Author : Jean-Michel Marin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-26
Bayesian Core A Practical Approach To Computational Bayesian Statistics written by Jean-Michel Marin 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 2007-05-26 with Mathematics categories.
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
Bayesian Data Analysis Second Edition
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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2003-07-29
Bayesian Data Analysis Second Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-29 with Mathematics categories.
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
Bayesian Statistics A Review
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Author : D. V. Lindley
language : en
Publisher: SIAM
Release Date : 1972-01-31
Bayesian Statistics A Review written by D. V. Lindley and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972-01-31 with Mathematics categories.
A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics.
Frontiers Of Statistical Decision Making And Bayesian Analysis
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Author : Ming-Hui Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-24
Frontiers Of Statistical Decision Making And Bayesian Analysis written by Ming-Hui Chen 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 2010-07-24 with Mathematics categories.
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.
Bayesian Statistics 9
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Author : José M. Bernardo
language : en
Publisher: Oxford University Press
Release Date : 2011-10-06
Bayesian Statistics 9 written by José M. Bernardo and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-06 with Mathematics categories.
The Valencia International Meetings on Bayesian Statistics - established in 1979 and held every four years - have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the authors(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, high lighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology. The Valencia 9 invited papers cover a broad range of topics, including foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.