Bayesian Inference Books

Get ready to dive into the exciting world of Bayesian inference books! This roundup features some of the most engaging and informative works on the subject. Whether you’re a beginner looking to grasp the basics or a seasoned statistician seeking advanced knowledge, you’re sure to find the perfect book to satisfy your curiosity.

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1. An Introduction to Bayesian Inference in Econometrics

An Introduction to Bayesian Inference in Econometrics is a groundbreaking work that explores the principles of Bayesian analysis, applying them to various aspects of economics. Pioneered by the esteemed econometrician Arnold Zellner, this comprehensive guide provides a deep understanding of Bayesian inference and its relevance in the field of economics.

Covering a broad range of topics including regression analysis, time series models, and simultaneous equation econometric models, this book serves as a valuable resource for scholars and practitioners alike. With its thorough explanations and practical applications, An Introduction to Bayesian Inference in Econometrics remains a staple in the study of Bayesian inference and econometrics.

• Authors: Arnold Zellner

• Publisher: New York : J. Wiley

• Published Date: November 26, 1971

• Page Count: 456.0

• Print Type: BOOK

• Categories: Mathematics

• Average Rating: 4.0

• Ratings Count: 1.0

• Maturity Rating: NOT_MATURE

• Language: en

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2. Bayesian Statistical Inference

Bayesian Statistical Inference, written by Gudmund R. Iversen, offers statisticians a comprehensive introduction to the theoretical importance of Bayesian inference, a methodology that has garnered significant attention but continues to face challenges in practice.

Iversen argues that the application of Bayesian inference has been hindered by empirical researchers’ lack of understanding of the methodology, and his volume serves as a valuable guide to remedy this issue. This succinct but enlightening work of 88 pages is a must-read for anyone seeking to deepen their knowledge of Bayesian statistical inference. Join Iversen in exploring the intricacies of this influential statistical approach, and gain a solid foundation in its principles.

Perfect for mathematics enthusiasts, Bayesian Statistical Inference is a key addition to your library and a useful companion to Iversen’s Tests of Significance.

• Authors: Gudmund R. Iversen

• Publisher: SAGE

• Published Date: November 30, 1984

• Page Count: 88.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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3. Perception as Bayesian Inference

Unravel the mysteries of visual perception through the lens of an innovative theoretical paradigm in “Perception as Bayesian Inference” by David C. Knill and Whitman Richards. Published by Cambridge University Press in 1996, this groundbreaking book offers a comprehensive understanding of the subject, merging experimental and computational insights for a riveting read.

Packed with 534 pages, this enlightening title invites readers to dive deep into the complex world of computers and visual cognition. By exploring the concept of Bayesian inference in the context of perception, the authors provide a thought-provoking exploration of how our brains process and interpret the world around us.

Don’t miss the chance to embark on this intellectually stimulating journey that seamlessly blends cutting-edge theories with real-world applications. “Perception as Bayesian Inference” is a must-read for those seeking to expand their knowledge of computer science and visual cognition.

• Authors: David C. Knill, Whitman Richards

• Publisher: Cambridge University Press

• Published Date: September 13, 1996

• Page Count: 534.0

• Print Type: BOOK

• Categories: Computers

• Maturity Rating: NOT_MATURE

• Language: en

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4. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

”Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives” is a comprehensive guide to statistical methods for handling missing data analysis. Authored by renowned researchers Andrew Gelman and Xiao-Li Meng, this book is an essential resource for both research and applications.

With a focus on real-world examples and missing data analysis techniques such as multiple imputation, propensity scores, and Bayesian inference, this book provides a practical approach to complex statistical issues. Its extensive coverage of intermediate and advanced statistical techniques makes it a valuable resource for professionals and students alike. The book also includes applications from the social and health sciences, highlighting the relevance of these methods in real-life scenarios.

With contributions from respected researchers, such as Professor Don Rubin, this book is a must-read for anyone seeking to gain expertise in the field of applied statistics and missing data analysis.

• Authors: Andrew Gelman, Xiao-Li Meng

• Publisher: John Wiley & Sons

• Published Date: September 03, 2004

• Page Count: 448.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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5. Bayesian Inference with Geodetic Applications

Delve into the world of Bayesian inference with this comprehensive guide, Bayesian Inference with Geodetic Applications. Author Karl-Rudolf Koch takes readers on a journey through the essential concepts, providing a firm grasp of Bayes’ theorem, prior density functions, point estimation, confidence regions, hypothesis testing, and predictive analysis. With a strong focus on practical applications, Monte Carlo methods are also discussed to facilitate the integration of the posterior distribution.

In this book, Bayesian inference is explored in the context of linear, nonlinear, mixed, and models with unknown variance and covariance components. Moreover, it covers classification, robust maximum likelihood type estimates, and digital image reconstruction. Ideal for professionals and students alike, Bayesian Inference with Geodetic Applications offers a valuable resource for those looking to understand and apply Bayesian inference in their work.

• Authors: Karl-Rudolf Koch

• Publisher: Springer

• Published Date: April 11, 2006

• Page Count: 205.0

• Print Type: BOOK

• Categories: Science

• Maturity Rating: NOT_MATURE

• Language: en

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6. Bayesian Inference for Gene Expression and Proteomics

Discover the intriguing world of Bayesian inference and its applications in gene expression and proteomics with Bayesian Inference for Gene Expression and Proteomics. Written by esteemed authors Kim-Anh Do, Peter Müller, and Marina Vannucci, this comprehensive guide offers expert insights into the Bayesian methodology, tools, and software essential for multi-platform high-throughput experimentation.

Delving into the complexities of these advanced techniques, the book provides a solid foundation for understanding the intricacies of gene expression and proteomics. With its engaging subject matter and comprehensive knowledge, Bayesian Inference for Gene Expression and Proteomics is a must-read for anyone seeking to master these critical scientific advancements.

• Authors: Kim-Anh Do, Peter Müller, Marina Vannucci

• Publisher: Cambridge University Press

• Published Date: July 24, 2006

• Page Count: 437.0

• Print Type: BOOK

• Categories: Mathematics

• Average Rating: 3.0

• Ratings Count: 1.0

• Maturity Rating: NOT_MATURE

• Language: en

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7. Bayesian Inference

With Ecological Applications

Bayesian Inference: With Ecological Applications is an essential read for environmental scientists, ecologists, wildlife biologists, and anyone looking to understand the power and usefulness of Bayesian methods in an ecological context. Written by renowned authors William A.

Link and Richard J. Barker and published by Academic Press, this comprehensive and accessible guide walks readers through the complex subject of Bayesian inference with engaging examples drawn from the fields of ecology and wildlife research. The book is supported by software examples and analytical tools on its companion website, making it an invaluable resource for students, researchers, and professionals alike.

With its clear and concise language, Bayesian Inference is the ideal introduction to this increasingly prevalent approach to inference.

• Authors: William A Link, Richard J Barker

• Publisher: Academic Press

• Published Date: August 07, 2009

• Page Count: 354.0

• Print Type: BOOK

• Categories: Science

• Maturity Rating: NOT_MATURE

• Language: en

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8. Bayesian Theory

”Bayesian Theory” by José M. Bernardo and Adrian F.

M. Smith is a comprehensive and highly regarded text in the field of statistics and decision analysis.

Now available in paperback, the book delves into the intricacies of Bayesian theory, offering readers an in-depth exploration of key concepts and theoretical results. The authors, firmly grounded in the Bayesian perspective, also provide an overview of non-Bayesian theories.

Each chapter critically examines controversial issues, making it an essential resource for students, researchers, and professionals across various disciplines, including mathematics, economics, business, and engineering. The text is accessible to those with advanced calculus knowledge, and the emphasis is placed on statistical concepts rather than abstract mathematics. This influential work is an indispensable guide for anyone seeking to deepen their understanding of Bayesian statistics.

• Authors: José M. Bernardo, Adrian F. M. Smith

• Publisher: John Wiley & Sons

• Published Date: September 25, 2009

• Page Count: 608.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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9. Bayesian Analysis for the Social Sciences

”Bayesian Analysis for the Social Sciences” by Simon Jackman is an essential read for social science students looking to expand their understanding of Bayesian methods. This comprehensive guide, published by John Wiley & Sons, offers a tailored approach to the subject, demonstrating how Bayesian analysis can be effectively utilized in political science, psychology, sociology, and economics.

The book is divided into an introductory section that provides the foundations of Bayesian inference, followed by explanations of various social science problems, such as proportions, cross-tabulations, counts, means, and regression analysis. It features plenty of real-world examples to illustrate these concepts, ensuring readers can grasp the practical implications of Bayesian analysis.

To complement the theory and examples, the author provides exercises in all chapters to reinforce the students’ understanding of the material. The book also features detailed descriptions of all the crucial concepts, making it an accessible read for those with a basic understanding of statistics, beyond a first course.

Another key aspect of this book is the inclusion of guidance on implementing the methods using WinBUGS, the most widely-used Bayesian analysis software, and R, an open-source statistical software. The support extends to a website where readers can access WinBUGS and R code, as well as data sets, further enhancing their learning experience. Overall, “Bayesian Analysis for the Social Sciences” is a valuable resource for students and professionals alike who want to harness the power of Bayesian methods in their social science research.

• Authors: Simon Jackman

• Publisher: John Wiley & Sons

• Published Date: October 27, 2009

• Page Count: 598.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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10. Frontiers of Statistical Decision Making and Bayesian Analysis

In Honor of James O. Berger

”Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger” is an insightful and comprehensive review of current research in Bayesian analysis and statistical decision theory. This book, authored by a team of renowned experts in the field, is a testament to the rapidly expanding and diverse nature of this interdisciplinary research area. It aims to provide readers with an understanding of the most pressing challenges and opportunities in Bayesian analysis, highlighting various methods and applications.

Covering topics such as objective Bayesian inference, shrinkage estimation, model selection and testing, nonparametric Bayes, data mining, and machine learning, this book presents a balance between theory and applications. It delves into various application areas, including computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling, and political science, finance, and marketing.

Designed to be a valuable resource for researchers, non-statisticians, graduate students, and research scholars in statistics or biostatistics, this book is a must-read for anyone seeking to stay informed about the current research frontiers in Bayesian analysis. With its engaging content and expansive scope, “Frontiers of Statistical Decision Making and Bayesian Analysis” is an essential addition to any professional’s library.

• Authors: Ming-Hui Chen, Peter Müller, Dongchu Sun, Keying Ye, Dipak K. Dey

• Publisher: Springer Science & Business Media

• Published Date: July 24, 2010

• Page Count: 631.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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11. Bayesian Inference in Statistical Analysis

”Bayesian Inference in Statistical Analysis” by George E. P.

Box and George C. Tiao is a comprehensive guide to understanding the application of Bayes’ theorem to scientific investigations that require inferences about parameter values with limited prior knowledge.

This book explores essential aspects of the Bayesian approach, including prior distribution choice, nuisance parameters, and sufficient statistics. It delves into standard problems related to location and scale parameters, and provides mathematical analyses illustrated by numerical examples. This well-researched work demonstrates the effectiveness of the Bayesian approach in various statistical scenarios, making it a valuable resource for mathematicians and statisticians alike.

• Authors: George E. P. Box, George C. Tiao

• Publisher: John Wiley & Sons

• Published Date: January 25, 2011

• Page Count: 608.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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12. Understanding Computational Bayesian Statistics

Understanding Computational Bayesian Statistics is a hands-on introduction to computational statistics like no other. Written from a unique Bayesian perspective, the book provides readers with a solid grounding in statistics and guides them through the cutting-edge approach of Bayesian inference.

With its hands-on approach and innovative techniques, the author showcases how to draw samples from the posterior distribution, even when the formula giving its shape is all that’s known. The text offers detailed insights into the most common statistical models, such as multiple linear regression, hierarchical mean models, logistic regression models, and proportional hazards models.

The book begins by outlining the similarities and differences between Bayesian and likelihood approaches to statistics, followed by essential techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and perform the Bayesian inference. Coverage includes drawing random samples from the posterior using reshaping methods, understanding distributions from the one-dimensional exponential family, and proficiency in Markov chains, Metropolis-Hastings algorithms, Gibbs sampling, and more.

Understanding Computational Bayesian Statistics is perfect for courses on computational statistics at upper-level undergraduate and graduate levels, as well as valuable research tools for researchers and practitioners who utilize computer programs to analyze data and solve complex problems.

• Authors: William M. Bolstad

• Publisher: John Wiley & Sons

• Published Date: September 20, 2011

• Page Count: 336.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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13. Bayesian Statistics and Marketing

Discover the power of Bayesian methods in marketing with the groundbreaking book, Bayesian Statistics and Marketing. With computational and modelling breakthroughs making it an ideal tool for a wide range of marketing problems, this book provides a comprehensive introduction to Bayesian methods for marketing researchers and practitioners alike.

Through detailed examples and case studies, the authors—leaders in the field—walk you through the nature of the computational revolution and its impact on marketing analysis. From new product introduction to pricing and with various data sources, Bayesian Statistics and Marketing demonstrates the benefits of Bayesian methods in addressing marketing challenges.

Emphasizing the basics of the Bayesian approach, the authors also discuss the self-contained introduction of Bayesian methods, ensuring accessibility to readers at all levels. By illustrating how Bayesian methods can be applied to essential marketing problems with the use of MCMC techniques, the book offers invaluable insights for graduate students, professors, and professionals in the ever-evolving landscape of marketing research.

With an accompanying R package, bayesm, featuring all the models and methods discussed in the book, and datasets and R code for the case studies available on the book’s website, readers are equipped with essential resources for practical application. Get ready to revolutionize your marketing research with Bayesian Statistics and Marketing, your unified reference for cutting-edge methods and insights.

• Authors: Peter E. Rossi, Greg M. Allenby, Rob McCulloch

• Publisher: John Wiley & Sons

• Published Date: May 14, 2012

• Page Count: 368.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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14. Bayesian Inference in Wavelet-Based Models

”Bayesian Inference in Wavelet-Based Models” is a comprehensive exploration of Bayesian methods for inference in the wavelet domain. This volume is meticulously divided into six enlightening parts that deep dive into various aspects of prior modeling, decision theoretic aspects of wavelet-based models, 2-dimensional wavelet decomposition in spatial modeling, and empirical Bayes estimation. The authors, Peter Müller and Brani Vidakovic, have masterfully compiled an informative collection of papers that serve to provide an in-depth understanding of this complex topic.

The valuable insights from these chapters are further enriched by a final section that highlights real-life case studies. The authors, publishers, and referees have worked tirelessly to ensure the timely delivery of this insightful book, making it a must-read for anyone interested in mathematics, particularly within the wavelet-based model domain.

• Authors: Peter Müller, Brani Vidakovic

• Publisher: Springer Science & Business Media

• Published Date: December 06, 2012

• Page Count: 406.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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15. Robust Bayesian Analysis

Robust Bayesian Analysis, by David Rios Insua and Fabrizio Ruggeri, offers a comprehensive guide to enhancing the efficacy of Bayesian analysis. The book delves into overcoming the dependence on subjective inputs, particularly the prior and loss, to evaluate the impact on the analysis results. Through the utilization of robust Bayesian methods, the authors empower the reader to determine whether the effects of the inputs are significant or negligible, thus guiding them in the refinement of the information and data available.

Presenting an overview of the current state of this innovative approach, Robust Bayesian Analysis serves as a valuable resource for applied settings and identifies vital areas for future research.

• Authors: David Rios Insua, Fabrizio Ruggeri

• Publisher: Springer Science & Business Media

• Published Date: December 06, 2012

• Page Count: 431.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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16. Bayesian Inference

Parameter Estimation and Decisions

Unravel the captivating world of Bayesian Inference with this comprehensive reference guide by Hanns L. Harney. In this must-read, you’ll discover the art of parameter estimation and decision-making.

The beauty of Bayesian Inference lies in its ability to solve the longstanding problem in the physical sciences, where data doesn’t follow the traditional Gaussian distribution. This text, a valuable resource for readers, introduces the concept at an introductory level, complete with numerous examples and exercises to help solidify your understanding.

Join the journey as we delve into the world of statistics, revealing the generalization of Gaussian error intervals for other types of data distributions. Bayesian Inference is not just a book, but a tool for unlocking the mysteries of the physical sciences through this compelling, yet accessible, narrative. So, buckle up and prepare for an enlightening experience!

• Authors: Hanns L. Harney

• Publisher: Springer Science & Business Media

• Published Date: March 14, 2013

• Page Count: 263.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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17. Introduction to Bayesian Statistics

”Break new ground in statistical analysis with Introduction to Bayesian Statistics. This comprehensive textbook takes a fresh approach to the topic, focusing on Bayesian methods that are becoming increasingly popular in various fields of applied statistics. With clear explanations and a user-friendly style, this book is a perfect fit for advanced undergraduate and graduate students as well as professionals seeking a practical guide to Bayesian statistics.

Discover the advantages of using Bayesian methods for inference and uncover how they compare favorably to frequentist alternatives. This Second Edition also provides additional chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations. Plus, it includes more exercises, a calculus refresher, and an introduction to using R functions and Minitab® macros for Bayesian analysis and Monte Carlo simulations.

Get a headstart in the world of Bayesian statistics with William M. Bolstad’s Introduction to Bayesian Statistics. This must-read text is a valuable resource for anyone looking to gain a deeper understanding of this essential statistical approach. “

• Authors: William M. Bolstad

• Publisher: John Wiley & Sons

• Published Date: June 05, 2013

• Page Count: 402.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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18. Bayesian Essentials with R

”Bayesian Essentials with R” is a comprehensive guide to computational Bayesian statistics, perfect for both academic and professional use. Written by Jean-Michel Marin and Christian P. Robert, this book provides a self-contained introduction to Bayesian modeling for those with an undergraduate background in probability and statistics.

The book focuses on standard statistical models, empowering readers to take part in real-life data analysis scenarios. It showcases the bayes package in R, which offers an operational methodology for Bayesian inference. Through hands-on examples, readers are guided towards effective programming of the methods presented in the book.

”Bayesian Essentials with R” includes practical applications of the concepts, as well as detailed discussions on R codes for an in-depth understanding. The authors also emphasize the importance of prior distributions and reference solutions for each model, providing a solid foundation for readers. This textbook can be used at both undergraduate and graduate levels, accommodating students in professional degree programs and scientists in data analysis.

With its fully revised edition, the book also addresses introductory courses on Bayesian statistics. The authors’ extensive experience in the field ensures a rich, engaging, and informative read for anyone interested in deepening their understanding of Bayesian methodology.

• Authors: Jean-Michel Marin, Christian P. Robert

• Publisher: Springer Science & Business Media

• Published Date: October 28, 2013

• Page Count: 296.0

• Print Type: BOOK

• Categories: Computers

• Maturity Rating: NOT_MATURE

• Language: en

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19. Bayesian Data Analysis, Third Edition

Discover the world of Bayesian data analysis through the third edition of this highly-regarded textbook. Written by an esteemed group of statisticians, Bayesian Data Analysis, Third Edition is a must-read for anyone looking to navigate the field of Bayesian methods.

This book sets itself apart by taking an applied approach to analysis, making it accessible to both experienced researchers and students alike. The authors introduce essential concepts from a data-analytic perspective, followed by advanced techniques. Throughout the text, you will find numerous worked examples, which emphasize the practical application of Bayesian inference in real-life situations.

The third edition features four new chapters on nonparametric modeling, a discussion on weakly informative priors, boundary-avoiding priors, and more. It also covers software code improvements, including advanced simulation techniques like Hamiltonian Monte Carlo, variational Bayes, and expectation propagation.

Designed for use by undergraduate students, graduate students, and researchers, this book is a comprehensive resource for learning and applying Bayesian data analysis. With additional materials available online, such as data sets used in examples, solved exercises, and software instructions, this text provides the perfect starting point for Bayesian inference.

Join the ranks of those who have benefited from Bayesian Data Analysis, Third Edition and delve into the world of data analysis and research problem-solving with this indispensable guide.

• Authors: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin

• Publisher: CRC Press

• Published Date: November 01, 2013

• Page Count: 677.0

• Print Type: BOOK

• Categories: Mathematics

• Average Rating: 5.0

• Ratings Count: 1.0

• Maturity Rating: NOT_MATURE

• Language: en

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20. Bayesian Inference for Partially Identified Models

Exploring the Limits of Limited Data

Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data, written by Paul Gustafson, is a comprehensive guide that explores the application of Bayesian inference in partially identified models (PIMs). Offering readers a robust understanding of PIMs, the book dives into the statistical theory, properties, and applications of PIMs.

Throughout the book, Gustafson explains how reparameterization can assist in computing posterior quantities, providing valuable insight into the properties of Bayesian estimators. The author then compares partial identification and model misspecification, outlining which issue poses a greater challenge. Next, readers will find a detailed exploration of PIM examples and the impact of partial identification on inferences. The book also reveals methods for characterizing the value of information obtained from data in a partially identified context and delves into some recent applications of PIMs.

In the final chapter, Gustafson reflects on the past and present state of research on partial identification. Overall, this book serves as an essential resource for readers interested in understanding how to use Bayesian methods for analyzing PIMs, offering insights into when a posterior distribution on a target parameter will be useful or less so.

• Authors: Paul Gustafson

• Publisher: CRC Press

• Published Date: April 01, 2015

• Page Count: 196.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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21. Nonparametric Bayesian Inference in Biostatistics

”Nonparametric Bayesian Inference in Biostatistics” is a comprehensive guide for experts and professionals working in the field of biostatistics. Co-authored by Riten Mitra and Peter Müller, it is published by Springer. The book presents the Nonparametric Bayesian (BNP) approach, an exciting method that is gaining prominence due to its ability to handle the abundance of data and work with complex probability models beyond the typical parametric approaches.

This technique has proven useful in clinical trials, spatial inference, and proteomics, among many other applications. BNP’s potential is not limited to survival analysis but extends to critical tasks like patient data segmentation and genome segmentation.

With its engaging style and practical examples that connect theory to practice, this book is an invaluable resource for researchers, students, and professionals seeking to harness the power of Nonparametric Bayesian approaches to make groundbreaking contributions to biostatistics.

• Authors: Riten Mitra, Peter Müller

• Publisher: Springer

• Published Date: July 25, 2015

• Page Count: 448.0

• Print Type: BOOK

• Categories: Medical

• Maturity Rating: NOT_MATURE

• Language: en

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22. Bayesian Methods for Hackers

Probabilistic Programming and Bayesian Inference

Dive into the fascinating world of Bayesian inference without the need for advanced mathematical analysis in “Bayesian Methods for Hackers” by Cameron Davidson-Pilon. This book, published by Addison-Wesley Professional, introduces Bayesian inference from a computational perspective, making it accessible to hackers and users with varying levels of mathematical understanding.

Through the use of powerful PyMC language and Python tools such as NumPy, SciPy, and Matplotlib, “Bayesian Methods for Hackers” demonstrates practical techniques and concepts of Bayesian inference. The book guides readers through building and training their first Bayesian model, opening the “black box” of the Markov Chain Monte Carlo algorithm, and applying Bayesian inference in various domains, including finance and marketing.

With coverage of essential concepts like clustering, convergence, autocorrelation, and thinning, this book provides valuable insights on Bayesian inference and its implementation through Python code. Whether you’re a hacker, data scientist, or someone interested in understanding Bayesian methods, “Bayesian Methods for Hackers” offers a comprehensive and engaging introduction to this powerful inference technique.

• Authors: Cameron Davidson-Pilon

• Publisher: Addison-Wesley Professional

• Published Date: September 30, 2015

• Page Count: 549.0

• Print Type: BOOK

• Categories: Computers

• Average Rating: 3.0

• Ratings Count: 1.0

• Maturity Rating: NOT_MATURE

• Language: en

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23. Learning Bayesian Models with R

Unlock the power of Bayesian Machine Learning with R - the essential toolbox for data scientists and analysts alike!

In Learning Bayesian Models with R, Dr. Hari M. Koduvely takes you on a journey through the fundamentals of Bayesian Inference, while demonstrating its practical applications using R programming. With a focus on real-world problem-solving, this book leads you through step-by-step exercises and practical examples to strengthen your grasp on Bayesian methods.

From mastering classification models and probabilistic theory to diving into deep learning frameworks, this guide equips you with the latest state-of-the-art techniques in Bayesian Machine Learning. You’ll learn to build predictive models that explore discrete variables, delve into advanced analytics applications, and analyze large data sets.

Learning Bayesian Models with R also covers essential practical exercises that provide hands-on experience with R packages and data sets from the UCI Machine Learning repository. As you progress, you’ll explore Bayesian Linear Regression, Bayesian Logistic Regression, and Bayesian Inference on Big Data - all using the power of R programming and its growing ecosystem of tools.

Finally, this book introduces you to the cutting-edge field of Deep Learning, providing insights into the most advanced neural network models currently at the forefront of Artificial Intelligence.

Packed with in-depth explanations and practical examples, Learning Bayesian Models with R is your go-to resource for unlocking the full potential of Bayesian Machine Learning in today’s fast-paced data-driven world.

• Authors: Dr. Hari M. Koduvely

• Publisher: Packt Publishing Ltd

• Published Date: October 28, 2015

• Page Count: 168.0

• Print Type: BOOK

• Categories: Computers

• Maturity Rating: NOT_MATURE

• Language: en

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24. Introduction to Bayesian Statistics

”Introduction to Bayesian Statistics” by William M. Bolstad and James M. Curran is a comprehensive guide to Bayesian inference, suitable for students and professionals alike. With four newly-added chapters, this third edition addresses the latest advances in the field of Bayesian statistics, making it an essential resource for those who seek to understand the growing importance of Bayesian methods in applied statistical analysis.

Covering topics ranging from introductory concepts like scientific data gathering and robust Bayesian methods, to advanced topics such as Bayesian inference for the Multiple Linear Regression Model and computational Bayesian statistics, this book provides a well-rounded introduction to Bayesian statistics. The authors’ clear and concise writing style allows readers to easily grasp complex concepts and apply them to real-world scenarios.

In addition to its comprehensive content, “Introduction to Bayesian Statistics” offers numerous exercise sets, updated to reflect new applications and the latest software applications. Accessible and user-friendly, this book comes complete with detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, making it an invaluable resource for anyone seeking to master Bayesian statistics.

Whether used as a textbook for upper-undergraduate or first-year graduate level courses, or as a reference work for statisticians, “Introduction to Bayesian Statistics” is an indispensable guide to the principles and applications of this powerful and growing field. Don’t miss your chance to dive into the world of Bayesian statistics with this definitive resource.

• Authors: William M. Bolstad, James M. Curran

• Publisher: John Wiley & Sons

• Published Date: September 02, 2016

• Page Count: 805.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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25. Bayesian Inference

Data Evaluation and Decisions

Discover the fascinating world of Bayesian inference with this groundbreaking new edition. Written by Hanns Ludwig Harney, Bayesian Inference: Data Evaluation and Decisions provides a comprehensive introduction to the analysis of data using Bayes rule. With a focus on situations where data follow distributions other than Gaussian, this approach is invaluable for determining the validity of theories, especially in situations where the observed parameter barely exceeds the background or when multiparametric histograms are sparsely populated.

Offering practical solutions to real-world problems, this book delves deeper into the logic of quantum mechanics, demonstrating that it’s rooted in the logic of unbiased inference from counting data. This edition features new sections on factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and incoherent alternatives, and fitting with multinomial distribution. Designed for advanced undergraduates and graduates in the physical sciences, Bayesian Inference is perfect for students working in or planning to enter fields such as medical physics, nuclear physics, quantum mechanics, and chaos.

Packed with additional problems and examples, Bayesian Inference: Data Evaluation and Decisions requires no prior knowledge of quantum mechanics and is easy to understand, featuring numerous examples and exercises. Ideal for anyone looking to expand their understanding of data analysis, Bayesian Inference is an essential resource for those interested in the physical sciences.

• Authors: Hanns Ludwig Harney

• Publisher: Springer

• Published Date: October 18, 2016

• Page Count: 243.0

• Print Type: BOOK

• Categories: Science

• Maturity Rating: NOT_MATURE

• Language: en

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26. Practical Bayesian Inference

Practical Bayesian Inference is a comprehensive guide for undergraduate and graduate students seeking to understand the fundamental concepts of probability and statistics, as well as the computational tools required to apply them. This essential resource, authored by renowned expert Coryn A. L.

Bailer-Jones, provides a solid foundation in Bayesian inference, making it an invaluable addition to any mathematics curriculum. With its engaging content and clear, concise explanations, Practical Bayesian Inference is a must-read for students and professionals alike who desire to master the art of probability and statistics.

• Authors: Coryn A. L. Bailer-Jones

• Publisher: Cambridge University Press

• Published Date: April 27, 2017

• Page Count: 306.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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27. Fundamentals of Nonparametric Bayesian Inference

With the rapid advancement of computing technology, Bayesian nonparametrics have become a versatile framework for inference, playing a vital role in multiple fields. “Fundamentals of Nonparametric Bayesian Inference” by Subhashis Ghosal and Aad van der Vaart is a comprehensive and authoritative text on Bayesian nonparametrics.

The authors, leading researchers in the field, bring together twenty years of theoretical advancements to cover key aspects of these methods, from prior construction to computation and large sample behavior of posteriors. The book is unique in its dedication to understanding the behavior of posteriors, critical for selecting effective priors, and presents precise sufficient conditions with complete proofs that guarantee desired posterior properties.

With each chapter concluding with historical notes and a variety of exercises, this well-rounded book is essential for graduate students and researchers in statistics, machine learning, econometrics, and biostatistics, offering in-depth knowledge of Bayesian nonparametrics.

• Authors: Subhashis Ghosal, Aad van der Vaart

• Publisher: Cambridge University Press

• Published Date: June 26, 2017

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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28. Bayesian Inference

Bayesian Inference: A Comprehensive Guide to Theory and Application

Are you seeking to understand the revolutionary methods that drive modern logistics, automotive safety, medical services, and search and rescue operations? Look no further than Bayesian Inference—a groundbreaking book that delves into the intricacies of this powerful technique and its far-reaching applications.

Written by renowned mathematician Javier Prieto Tejedor, Bayesian Inference provides an accessible entry into the field for both beginners and experts alike. Whether you’re a budding data scientist or an experienced mathematician looking to deepen your understanding, this book offers invaluable insights into the world of Bayesian inference.

Beginning with a survey of the varied and ever-growing range of Bayesian inference algorithms, the author introduces the reader to the ever-evolving field. Exploring the practical implementations of these techniques in a multitude of domains, Bayesian Inference showcases how this powerful approach has transformed the way we approach problems in life sciences, engineering, and economics.

Moving from theory to practice, the book highlights numerous real-world applications, from logistics to medical services, and from search and rescue operations to automotive safety. It acts as a critical resource for researchers, professionals, and students alike, as they delve into the fascinating realm of probability theory and derive the benefits of its sophisticated techniques.

With its comprehensive coverage of both foundational concepts and applications across multiple industries, Bayesian Inference is a must-read for anyone interested in this exciting and evolving field. Are you ready to become an expert in Bayesian inference? Don’t miss out – get your copy of Bayesian Inference today.

• Authors: Javier Prieto Tejedor

• Publisher: BoD – Books on Demand

• Published Date: November 02, 2017

• Page Count: 379.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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29. Bayesian Inference for Stochastic Processes

”Bayesian Inference for Stochastic Processes” offers readers a deep dive into the fascinating world of stochastic processes and their Bayesian analysis. Authored by Lyle D. Broemeling, this comprehensive book is a must-read for those eager to learn the foundational concepts of stochastic processes and Bayesian inference.

The book begins by providing an introduction to Bayesian inference and its advantages, including the optimal use of prior information. To facilitate a thorough understanding, the author employs numerous examples relevant to stochastic processes with a particular emphasis on the four major types - those with discrete time and state space, as well as those with continuous time and state space.

Following this, the book delves into the crucial elements necessary for understanding stochastic processes, effectively laying the groundwork for subsequent chapters. Among these chapters, readers can expect an elaborate explanation of Bayesian analysis for various types of stochastic processes, including discrete time Markov chains, Markov jump processes, normal processes, time series, and point and spatial processes.

To reinforce the core concepts and further engage readers, the book incorporates numerous examples drawn from scientific disciplines such as biology, economics, astronomy, and more. Illustrative WinBUGS and R code are also provided for the reader’s benefit.

Suited for readers with a solid background in probability theory and statistical inference, this proficient book will undoubtedly prove beneficial for scholars eager to gain hands-on experience with Bayesian inference for stochastic processes.

• Authors: Lyle D. Broemeling

• Publisher: CRC Press

• Published Date: December 12, 2017

• Page Count: 373.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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30. Bayesian Inference

Observations and Applications

Experience the cutting-edge world of Bayesian Inference with this comprehensive guide. Bayesian Inference: Observations and Applications delves into the realm of a-priori distributions, exploring both standard probability distributions and the more advanced fuzzy a-priori densities. The authors expertly guide readers through the complex process of fuzzy Bayesian inference, its applications, and the resulting decision analytic framework.

Not only does the book discuss inference, experimental design, and risk aversion, but it also showcases the power of sparse and structured sparse Bayesian methods. Through the use of probabilistic models, these techniques grant access to a wealth of flexible modeling capability and statistical information.

Bayesian Inference: Observations and Applications is an invaluable resource for those seeking a nuanced understanding of Bayesian inference and its applications in various domains such as sensor array signal processing. Prepare to embark on a fascinating journey through the intricacies of mathematical modeling and Bayesian inference.

• Authors: Rosario O. Cardenas

• Published Date: March 30, 2018

• Print Type: BOOK

• Categories: MATHEMATICS

• Maturity Rating: NOT_MATURE

• Language: en

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31. A Student’s Guide to Bayesian Statistics

Introducing “A Student’s Guide to Bayesian Statistics” - the ultimate resource for anyone looking to delve into the fascinating world of Bayesian statistics. Written by renowned author Ben Lambert and published by SAGE, this comprehensive book offers a student-friendly approach to understanding the complexities of Bayesian inference.

With a focus on making the subject accessible, the book is filled with engaging learning features, exercises, and visual elements that make the concepts easier to understand. Moreover, it offers online video tutorials and interactive simulations to further solidify your knowledge.

Covering a wide range of topics, this guide takes you on a journey from the basics of probability and Bayesian inference to advanced techniques like regression analysis and hierarchical methods. With a logical structure that gradually builds upon key concepts, you’ll find it easy to grasp the intricacies of Bayesian analytics.

While maintaining technical integrity, the book avoids making the subject overly simplistic. Instead, it provides clear, student-friendly language that anyone new to Bayesian statistics can easily follow. Additionally, it introduces you to essential software tools like R and Stan, helping you apply your newfound knowledge in practical settings.

”A Student’s Guide to Bayesian Statistics” is not only an educational tool but also a confidence-builder. As you work through the book, you’ll gain the skills and statistical confidence needed to apply the Bayesian formula in a variety of real-world scenarios.

Whether you’re a beginner or a seasoned statistician, “A Student’s Guide to Bayesian Statistics” offers invaluable insights into this exciting field. With its wealth of resources and comprehensive coverage, this book is sure to become an indispensable part of your statistical journey. Don’t miss out on the opportunity to explore the world of Bayesian statistics like never before!

• Authors: Ben Lambert

• Publisher: SAGE

• Published Date: April 20, 2018

• Page Count: 744.0

• Print Type: BOOK

• Categories: Social Science

• Maturity Rating: NOT_MATURE

• Language: en

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32. Computational Bayesian Statistics

Dive into the world of advanced Bayesian methods with ‘Computational Bayesian Statistics’, a comprehensive and integrated guide to this intriguing field. Authored by Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller, this book serves as your key to unlocking the powerful principles of Bayesian statistics.

Filled with real-world examples, ‘Computational Bayesian Statistics’ takes you on a journey from the fundamentals to the cutting edge of computational and software-driven Bayesian techniques. Don’t be intimidated by the complexities of the subject; this book breaks down the concepts in a way that is accessible and relatable.

As part of this journey, you’ll learn the latest techniques for dealing with complex data and making accurate predictions. The authors guide you through the intricacies of Bayesian methods, providing a solid understanding of the theory while at the same time equipping you with practical tools for real-world implementation.

With its balance of theoretical knowledge and practical experience, ‘Computational Bayesian Statistics’ stands as a valuable resource for anyone interested in this dynamic and influential area of statistical science. Whether you’re a scholar, a practitioner, or just someone curious about the world of Bayesian statistics, this book is guaranteed to intrigue and inspire.

• Authors: M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller

• Publisher: Cambridge University Press

• Published Date: February 28, 2019

• Page Count: 256.0

• Print Type: BOOK

• Categories: Business & Economics

• Maturity Rating: NOT_MATURE

• Language: en

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33. Bayesian Statistics for Beginners

A Step-by-step Approach

”Bayesian Statistics for Beginners: A Step-by-step Approach” is designed to make Bayesian statistics accessible for anyone with little or no background in math or statistics. Written in a casual, conversational tone, this book by authors Therese M. Donovan and Ruth M.

Mickey leads readers through various sample problems, allowing them to grasp the vocabulary, notation, and calculations required for Bayesian problems. As an entry-level resource, it successfully demystifies Bayesian statistics, making it a valuable guide for those seeking an introduction to this fascinating field.

• Authors: Therese M. Donovan, Ruth M. Mickey

• Publisher: Oxford University Press, USA

• Published Date: March 30, 2019

• Page Count: 430.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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34. Bayesian inference with INLA

Bayesian Inference with INLA is a comprehensive guide to the Integrated Nested Laplace Approximation (INLA) method, a groundbreaking computational technique for fitting Bayesian models. Written by Virgilio Gomez-Rubio, this book provides both an in-depth understanding of the methodology and practical guidance on how to use the INLA package in R for model fitting.

This book introduces INLA as a tool to tackle latent Gaussian Markov random fields models, which can often be computationally intensive when using traditional Markov chain Monte Carlo (MCMC) methods. By exploiting the conditional independence properties in the model, INLA offers a significant speed advantage while still providing accurate marginal inference on the model parameters.

The book covers a wide range of topics, including generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Moreover, it offers advanced features of the INLA package, such as how to extend available priors and latent models in the package.

Fully reproducible examples and datasets are provided in the book, with R code available on the book’s website. The examples span various fields, such as biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences, making it relevant for researchers from diverse backgrounds and specializations.

Whether you’re a seasoned statistician or a Bayesian inference enthusiast looking to expand your repertoire, Bayesian Inference with INLA serves as an invaluable resource for mastering this cutting-edge computational method and applying it to a multitude of real-world problems.

• Authors: Virgilio Gomez-Rubio

• Publisher: CRC Press

• Published Date: February 20, 2020

• Page Count: 316.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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35. Likelihood and Bayesian Inference

With Applications in Biology and Medicine

Delve into the world of modern statistical methods with “Likelihood and Bayesian Inference: With Applications in Biology and Medicine” by authors Leonhard Held and Daniel Sabanés Bové. This visually engaging textbook brings together the latest techniques in applied quantitative research, focusing on likelihood-based inference, Bayesian inference, and advanced topics like model choice and prediction. The authors seamlessly merge statistical theory with practical applications in medicine, epidemiology, and biology, making it a must-read for graduate students and researchers.

With new content on Markov models for time series analysis and a comprehensive appendix covering prerequisites in various disciplines, this book serves as a valuable resource for those seeking to understand and apply these powerful statistical methods. Get ready to enhance your analytical skills and broaden your knowledge with this comprehensive guide!

• Authors: Leonhard Held, Daniel Sabanés Bové

• Publisher: Springer Nature

• Published Date: March 31, 2020

• Page Count: 409.0

• Print Type: BOOK

• Categories: Medical

• Maturity Rating: NOT_MATURE

• Language: en

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36. Bayesian Inference on Complicated Data

Bayesian Inference on Complicated Data is a comprehensive guide for those seeking to understand and apply Bayesian inference techniques in various fields. Authored by Niansheng Tang, this book delves into the crucial concepts of Bayesian sampling methods, estimation, and prior selection. It offers a detailed analysis of the impact of the choice of the prior on Bayesian statistics, advances on Bayesian sampling methods, and in-depth explorations of Bayesian inference for complex datasets.

The book includes numerous examples and real-life case studies, such as breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data, making it an invaluable resource for statisticians, engineers, doctors, and machine learning researchers. With its concise structure and comprehensive coverage, Bayesian Inference on Complicated Data is an essential addition to any professional’s library in the field of mathematics.

• Authors: Niansheng Tang

• Publisher: BoD – Books on Demand

• Published Date: July 15, 2020

• Page Count: 120.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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37. Bayesian Statistics for Experimental Scientists

A General Introduction Using Distribution-Free Methods

”Bayesian Statistics for Experimental Scientists” is a must-read for any researcher looking to gain a better understanding of experimental data and statistical analysis. This invaluable resource offers an engaging introduction to Bayesian statistical inference, focusing on nonparametric and distribution-free methods. Drawing on real-life scientific examples, the author Richard A.

Chechile highlights the limitations of orthodox frequentist approaches, demonstrating how the Bayesian method excels at providing accurate and reliable results. With its comprehensive yet concise writing style and informative content, this book is an indispensable tool for experimental scientists seeking to enhance their data analysis skills and gain a competitive edge in their field. Don’t miss the opportunity to explore a revolutionary approach to statistical analysis – “Bayesian Statistics for Experimental Scientists” is a game-changer in the realm of mathematics and experimental research.

• Authors: Richard A. Chechile

• Publisher: MIT Press

• Published Date: September 08, 2020

• Page Count: 473.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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38. Bayesian Thinking in Biostatistics

Bayesian Thinking in Biostatistics is a comprehensive guide to Bayesian methodologies in biomedical research, written by experienced authors Gary L. Rosner, Purushottam W. Laud, and Wesley O. Johnson. This book provides an in-depth overview of Bayesian statistics and its applications, including rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests.

With a focus on sensible prior distributions and recent developments in Bayesian methodologies, this text highlights advances in clinical trial design and goes beyond standard statistical models by introducing Bayesian nonparametric methods. Emphasizing the estimation of biomedically relevant quantities and assessing uncertainty, the authors provide programs in the BUGS language (with variants for JAGS and Stan) that readers can use or adapt for their own research.

Bayesian Thinking in Biostatistics is suitable for graduate students in biostatistics, epidemiology, and biomedical researchers. With numerous exercises, real-data examples, and professional advice, this book serves as an essential text for learning and mastering Bayesian approaches in biomedical research.

• Authors: Gary L Rosner, Purushottam W. Laud, Wesley O. Johnson

• Publisher: CRC Press

• Published Date: March 16, 2021

• Page Count: 564.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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39. An Introduction to Bayesian Inference, Methods and Computation

Discover the fundamentals of Bayesian statistical methods with Nick Heard’s “An Introduction to Bayesian Inference, Methods and Computation. ” In this rapidly accessible lecture notes, Heard guides you through the philosophical and practical aspects of Bayesian inference. From the foundations of the prior/likelihood model construction, to advanced techniques such as nonparametrics, Gaussian processes, and latent factor models, these essential concepts are clearly explained and seamlessly integrated with practical applications. Featuring computer code samples written in Python and Stan, these methodologies are simple to learn and highly effective.

As you progress, you’ll also gain valuable knowledge on how to assess model fit and confidently choose between rival modelling approaches. Despite its brevity, this book packs a substantial punch, providing an invaluable introduction to the Bayesian approach, essential for mathematicians and data scientists alike.

• Authors: Nick Heard

• Publisher: Springer Nature

• Published Date: October 17, 2021

• Page Count: 177.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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40. Bayesian Inference of State Space Models

Kalman Filtering and Beyond

Embrace the power of Bayesian estimation and forecasting with this comprehensive guide to state space models, featuring Kalman filtering and beyond. Written by Kostas Triantafyllopoulos, Bayesian Inference of State Space Models: Kalman Filtering and Beyond delves into univariate and multivariate models, linear Gaussian, non-linear, and non-Gaussian models.

Join the journey through signal processing, environmetrics, economics, and systems engineering with this up-to-date and self-contained textbook. Explore real-life data sets and R code, while engaging with numerous exercises in each chapter. Suitable for upper undergraduate and graduate courses, this essential resource for students and scientists in statistics, systems engineering, signal processing, data science, finance, and econometrics is waiting to ignite your interest in state space models.

• Authors: Kostas Triantafyllopoulos

• Publisher: Springer Nature

• Published Date: November 12, 2021

• Page Count: 503.0

• Print Type: BOOK

• Categories: Mathematics

• Maturity Rating: NOT_MATURE

• Language: en

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