This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods.

Author: Peter D. Hoff

Publisher: Springer Science & Business Media

ISBN: 0387924078

Category: Mathematics

Page: 272

View: 309

A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Author: Cram101 Textbook ReviewsPublish On: 2009-01-01

Never HIGHLIGHT a Book Again!

Author: Cram101 Textbook Reviews

Publisher: Academic Internet Pub Incorporated

ISBN: 161744152X

Category: Education

Page: 86

View: 161

Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780387922997 .

"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels.

Author: William M. Bolstad

Publisher: John Wiley & Sons

ISBN: 9781118091562

Category: Mathematics

Page: 624

View: 215

"...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.

"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels.

Author: William M. Bolstad

Publisher: John Wiley & Sons

ISBN: 9781118593226

Category: Mathematics

Page: 624

View: 133

"...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.

A First Course in Bayesian Statistical Methods. Springer-Verlag; 2009 [22] Leonardo T, Hsu JSJ. Bayesian Methods. Cambridge: Cambridge University Press; 1999 [34] Ahmad K, Ahmad SP, Ahmed A. Classical and Bayesian approach in estimation ...

Author: Jan Peter Hessling

Publisher: BoD – Books on Demand

ISBN: 9781789239973

Category: Mathematics

Page: 158

View: 192

Statistical practices have recently been questioned by numerous independent authors, to the extent that a significant fraction of accepted research findings can be questioned. This suggests that statistical methodologies may have gone too far into an engineering practice, with minimal concern for their foundation, interpretation, assumptions, and limitations, which may be jeopardized in the current context. Disguised by overwhelming data sets, advanced processing, and stunning presentations, the basic approach is often intractable to anyone but the analyst. The hierarchical nature of statistical inference, exemplified by Bayesian aggregation of prior and derived knowledge, may also be challenging. Conceptual simplified studies of the kind presented in this book could therefore provide valuable guidance when developing statistical methodologies, but also applying state of the art with greater confidence.

Author: M. Antónia Amaral TurkmanPublish On: 2019-02-28

This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes.

Author: M. Antónia Amaral Turkman

Publisher: Cambridge University Press

ISBN: 9781108574617

Category: Mathematics

Page:

View: 712

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Jaynes was one of the most ardent supporters of objective Bayesian statistics. ... Sir Harold Jeffreys is the first statistician who exclusively used Bayesian methods for inference. Jeffreys also invented the Bayesian hypothesis test, ...

Author: Michael D. Lee

Publisher: Cambridge University Press

ISBN: 9781107653917

Category: Psychology

Page: 265

View: 951

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Author: Prabhanjan N. TattarPublish On: 2016-03-15

Available at http://www.multivariatestatistics.org. Grimmet, G. and Stirzaker, D. (2001). ... Monte Carlo Sampling Methods Using Markov Chains and Their Application. ... A First Course in Bayesian Statistical Methods. Springer.

Author: Prabhanjan N. Tattar

Publisher: John Wiley & Sons

ISBN: 9781119152736

Category: Computers

Page: 696

View: 542

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

A first course in Bayesian statistical methods. New York: Springer. Jo, B., & Muthén, B. (2001). Modeling of intervention effects with noncompliance: A latent variable modeling approach for randomized trials.

Author: Rick H. Hoyle

Publisher: Guilford Publications

ISBN: 9781462516797

Category: Psychology

Page: 740

View: 568

The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.

Monte Carlo sampling methods using Markov chains and their application. Biometrika, 57, 97–109. Hoff, P. (2009). A First Course in Bayesian Statistical Methods. Springer. Jona Lasinio, G., Mastrantonio, G., and Pollice, A. (2012).

Author: Marta Blangiardo

Publisher: John Wiley & Sons

ISBN: 9781118950210

Category: Mathematics

Page: 320

View: 493

Spatial and Spatio-Temporal Bayesian Models withR-INLA provides a much needed, practically oriented& innovative presentation of the combination of Bayesianmethodology and spatial statistics. The authors combine anintroduction to Bayesian theory and methodology with a focus on thespatial and spatio-temporal models used within the Bayesianframework and a series of practical examples which allow the readerto link the statistical theory presented to real data problems. Thenumerous examples from the fields of epidemiology, biostatisticsand social science all are coded in the R package R-INLA, which hasproven to be a valid alternative to the commonly used Markov ChainMonte Carlo simulations

Statistical Science 14, 382–417. Hoff, P. D. (2007). Extending the rank likelihood for semiparametric copula estimation. Annals of Applied Statistics 1, 265–283. Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods.

Author: Andrew Gelman

Publisher: CRC Press

ISBN: 9781439898208

Category: Mathematics

Page: 675

View: 908

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Author: Mani LakshminarayananPublish On: 2019-11-07

Use of Bayesian statistics in drug development: Advantages and challenges. ... Practical data analysis with JAGS using R. http://bendixcarstensen.com/Bayes/Cph2012/pracs.pdf. ... A First Course in Bayesian Statistical Methods.

Author: Mani Lakshminarayanan

Publisher: CRC Press

ISBN: 9781351584173

Category: Business & Economics

Page: 532

View: 581

The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2.6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development, from discovery, to clinical trial, to manufacturing with practical examples. This book will have a wide appeal to statisticians, scientists, and physicians working in drug development who are motivated to accelerate and streamline the drug development process, as well as students who aspire to work in this field. The advantages of this book are: Provides motivating, worked, practical case examples with easy to grasp models, technical details, and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting, as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles, technical reports, and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University, Dallas, Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.

Hoff, P. D. (2009), A first Course in Bayesian Statistical Methods, Springer: New York. Mohanty, S. P., S. Suresh Babu, , and N. Sreekumaran Nair, (2001), The Use of Arm Span as a Predictor of Height: A Study of South Indian women, ...

Author: Jim Albert

Publisher: CRC Press

ISBN: 9781351030120

Category: Mathematics

Page: 538

View: 357

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book.

Journal of the Royal Statistical Society: Series B, 34, ... Methods for the analysis of parallel streams of continuously recorded social behaviors. Psychological Bulletin, 105(3), ... A first course in Bayesian statistical methods.

Author: Deborah Fish Ragin

Publisher: Routledge

ISBN: 9780429948565

Category: Psychology

Page: 442

View: 748

In this comprehensive handbook, Ragin and Keenan present an all-encompassing analysis of the variety of different methods used in health psychology research. Featuring interdisciplinary collaborations from leading academics, this meticulously written volume is a guide to conducting cutting-edge research using tested and vetted best practices. It explains important research techniques, why they are selected and how they are conducted. The book critically examines both cutting-edge methods, such as those used in NextGen genetics, nudge theory, and the brain’s vulnerability to addiction, as well as the classic methods, including cortisol measurement, survey, and environmental study. The topics of the book span the gamut of health psychology field, from neuroimaging and statistical analysis to socioeconomic issues such as the policies used to address diseases in Africa, anti-vaxers, and the disproportionate impact of climate change on impoverished people. With each section featuring examples of best research practices, recommendations for study samples, accurate use of instrumentation, analytical techniques, and advanced-level data analysis, this book will be an essential text for both emerging student researchers and experts in the field and an indispensable resource in health psychology programs.

He has also worked on nonparametric Bayesian inference in the context of convex models and cluster analysis. His recent book “A First Course in Bayesian Statistical Methods” (2009) is in its second printing.

Author: Alan Agresti

Publisher: Springer Science & Business Media

ISBN: 9781461436492

Category: Mathematics

Page: 564

View: 309

Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.

Hoff, P. D. (2009), A First Course in Bayesian Statistical Methods, Springer. Hopper, J. L. and Seeman, E. (1994), 'The bone density of female twins discordant for tobacco use', New England Journal of Medicine 330, 387–392.

Author: Paul Gustafson

Publisher: CRC Press

ISBN: 9781439869406

Category: Mathematics

Page: 196

View: 518

Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification. This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.

The Annals of Applied Statistics, 4(2):1360–1383. ... Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. CRC Press, Boca Raton. ... A First Course in Bayesian Statistical Methods.

Author: John Bryant

Publisher: CRC Press

ISBN: 9780429841347

Category: Mathematics

Page: 280

View: 230

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty. The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com. "This book will be welcome for the scientific community of forecasters...as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'études démographiques

Journal of Environmental and Ecological Statistics, 5, 173– 190. ... Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems (with ... A First Course in Bayesian Statistical Methods.

Author: Sudipto Banerjee

Publisher: CRC Press

ISBN: 9781439819180

Category: Mathematics

Page: 584

View: 633

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. New to the Second Edition New chapter on spatial point patterns developed primarily from a modeling perspective New chapter on big data that shows how the predictive process handles reasonably large datasets New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling New chapter on the theoretical aspects of geostatistical (point-referenced) modeling Greatly expanded chapters on methods for multivariate and spatiotemporal modeling New special topics sections on data fusion/assimilation and spatial analysis for data on extremes Double the number of exercises Many more color figures integrated throughout the text Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages The Only Comprehensive Treatment of the Theory, Methods, and Software This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.

A First Course in Bayesian Statistical Methods. Springer, New York, 2009. [167] M. D. Hoffman and A. Gelman. The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, ...

Author: Gary L Rosner

Publisher: CRC Press

ISBN: 9781000352948

Category: Mathematics

Page: 621

View: 451

Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book ...is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments...are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course..." -Thomas Louis, Johns Hopkins University "The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems....Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems." - Peter Mueller, University of Texas With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests. Key Features Applies a Bayesian perspective to applications in biomedical science Highlights advances in clinical trial design Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in general Authors Gary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is professor Emeritus in the Department of Statistics as the University of California, Irvine.

Author: Magdalena Niewiadomska-BugajPublish On: 2020-12-09

Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge ... Probability and Statistics for Engineering and the Sciences, 3rd ed. Brooks and Cole, Pacific Grove, ... A First Course in Bayesian Statistical Methods.

Author: Magdalena Niewiadomska-Bugaj

Publisher: John Wiley & Sons

ISBN: 9781119243823

Category: Mathematics

Page: 592

View: 204

Updated classic statistics text, with new problems and examples Probability and Statistical Inference, Third Edition helps students grasp essential concepts of statistics and its probabilistic foundations. This book focuses on the development of intuition and understanding in the subject through a wealth of examples illustrating concepts, theorems, and methods. The reader will recognize and fully understand the why and not just the how behind the introduced material. In this Third Edition, the reader will find a new chapter on Bayesian statistics, 70 new problems and an appendix with the supporting R code. This book is suitable for upper-level undergraduates or first-year graduate students studying statistics or related disciplines, such as mathematics or engineering. This Third Edition: Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics Includes 650 problems and over 400 examples - an excellent resource for the mathematical statistics class sequence in the increasingly popular "flipped classroom" format Offers students in statistics, mathematics, engineering and related fields a user-friendly resource Provides practicing professionals valuable insight into statistical tools Probability and Statistical Inference offers a unique approach to problems that allows the reader to fully integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.