New Frontiers In Bayesian Statistics
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New Frontiers In Bayesian Statistics
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Author : Raffaele Argiento
language : en
Publisher: Springer Nature
Release Date : 2022-11-26
New Frontiers In Bayesian Statistics written by Raffaele Argiento and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-26 with Mathematics categories.
This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics.
New Frontiers In Bayesian Statistics
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Author : Raffaele Argiento
language : en
Publisher:
Release Date : 2022
New Frontiers In Bayesian Statistics written by Raffaele Argiento and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian 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.
New Frontiers Of Biostatistics And Bioinformatics
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Author : Yichuan Zhao
language : en
Publisher: Springer
Release Date : 2018-12-05
New Frontiers Of Biostatistics And Bioinformatics written by Yichuan Zhao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-05 with Mathematics categories.
This book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners.
Frontiers Of Statistical Decision Making And Bayesian Analysis
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Author : Ming-Hui Chen
language : en
Publisher: Springer
Release Date : 2010-08-05
Frontiers Of Statistical Decision Making And Bayesian Analysis written by Ming-Hui Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-05 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.
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.
Frontiers In Statistics
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Author : Jianqing Fan
language : en
Publisher: World Scientific
Release Date : 2006-07-17
Frontiers In Statistics written by Jianqing Fan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-07-17 with Computers categories.
During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics. Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets. This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel's distinguished contributions. Contents:Our Steps on the Bickel Way (K Doksum & Y Ritov)Semiparametric Models: A Review of Progress since BKRW (1993) (J A Wellner et al.)Efficient Estimator for Time Series (A Schick & W Wefelmeyer)On the Efficiency of Estimation for a Single-Index Model (Y Xia & H Tong)Estimating Function Based Cross-Validation (M J Van der Laan & D Rubin)Powerful Choices: Tuning Parameter Selection Based on Power (K Doksum & C Schafer)Nonparametric Assessment of Atypicality (P Hall & J W Kay)Selective Review on Wavelets in Statistics (Y Wang)Model Diagnostics via Martingale Transforms: A Brief Review (H L Koul)Boosting Algorithms: With an Application to Bootstrapping Multivariate Time Series (P Bühlmann & R W Lutz)Bootstrap Methods: A Review (S N Lahiri)An Expansion for a Discrete Non-Lattice Distribution (F Götze & W R van Zwet)An Overview on Nonparametric and Semiparametric Techniques for Longitudinal Data (J Fan & R Li)Regressing Longitudinal Response Trajectories on a Covariate (H-G Müller & F Yao)Statistical Physics and Statistical Computing: A Critical Link (J D Servidea & X-L Meng)Network Tomography: A Review and Recent Developments (E Lawrence et al.)Likelihood Inference for Diffusions: A Survey (Y Aït-Sahalia)Nonparametric Estimation of Production Efficiency (B U Park et al.)Convergence and Consistency of Newton's Algorithm for Estimating Mixing Distribution (J K Ghosh & S T Tokdar)Mixed Models: An Overview (J Jiang & Z Ge)Robust Location and Scatter Estimators in Multivariate Analysis (Y Zuo)Estimation of the Loss of an Estimate (W H Wong) Readership: Advanced graduate students and researchers in statistics. Keywords:Semiparametrics;Financial Econometrics;Longitudinal Data;Efficient Estimation;Single Index;Atypicality;Martingale Transforms;Boosting;Non-Lattice Distributions;Longitudinal Data;Network Tomography;Mixed ModelsKey Features:Gathers contributions from renowned researchers such as Kjell Doksum and Peter HallA must-have volume for researchers in statistics
Bayesian Statistics For Beginners
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Author : Therese M. Donovan
language : en
Publisher: Oxford University Press
Release Date : 2019-05-23
Bayesian Statistics For Beginners written by Therese M. Donovan 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 2019-05-23 with Mathematics categories.
Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.
Bayesian Essentials With R
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Author : Jean-Michel Marin
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-28
Bayesian Essentials With R 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 2013-10-28 with Computers categories.
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. 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. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
Bayesian Reasoning In Data Analysis A Critical Introduction
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Author : Giulio D'agostini
language : en
Publisher: World Scientific
Release Date : 2003-06-13
Bayesian Reasoning In Data Analysis A Critical Introduction written by Giulio D'agostini and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-13 with Mathematics categories.
This book provides a multi-level introduction to Bayesian reasoning (as opposed to “conventional statistics”) and its applications to data analysis. The basic ideas of this “new” approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide — under well-defined assumptions! — with “standard” methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.