Bayesian inference Books

Book SynopsisBayesian Statistics the Fun Way gets you understanding the theory behind data analysis without making you slog through a load of dry concepts first - with no programming experience necessary. You'll learn about probability with LEGO, statistics through Star Wars, distributions with bomb fuses, estimation through precipitation, and come away with some strong mathematical reasoning skills. This is a super approachable book for people who need to do data science and probability work in their lives, but never got a good grip on the underlying theory.Trade Review"An excellent introduction to subjects critical to all data scientists."—Inside Big Data"The author uses great examples to clarify key concepts . . . I would highly recommend as a supplement for any stats student or professional looking to refresh on Bayesian statistics." —Stan T., Design Collective

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Book SynopsisDrawing on primary source material and interviews with statisticians and other scientists, this book offers an account of Bayes' rule for general readers, It traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace.Trade Review"If you're not thinking like a Bayesian, perhaps you should be."—John Allen Paulos, New York Times Book Review"A masterfully researched tale of human struggle and accomplishment . . . Renders perplexing mathematical debates digestible and vivid for even the most lay of audiences."—Michael Washburn, Boston Globe"[An] engrossing study. . . . Her book is a compelling and entertaining fusion of history, theory and biography."—Ian Critchley, Sunday Times"This account of how a once reviled theory, Baye’s rule, came to underpin modern life is both approachable and engrossing."—Sunday Times"Makes the theory come alive . . . enjoyable . . . densely packed and engaging . . . very accessible . . . an admirable job of giving a voice to the scores of famous and non-famous people and data who contributed, for good or for worse."—Significance Magazine"A very compelling documented account . . . very interesting reading."—José Bernardo, Valencia List Blog"McGrayne explains [it] beautifully. . . . Top holiday reading."—The Australian"Engaging . . . Readers will be amazed at the impact that Bayes' rule has had in diverse fields, as well as by its rejection by too many statisticians. . . . I was brought up, statistically speaking, as what is called a frequentist. . . . But reading McGrayne's book has made me determined to try, once again, to master the intricacies of Bayesian statistics. I am confident that other readers will feel the same."—The Lancet"The Theory That Would Not Die is a rollicking tale of the triumph of a powerful mathematical tool."—Andrew Robinson, Nature"The Theory That Would Not Die is the first popular science book to document the rocky story of Bayes’s rule. At times, her tale has everything you would expect of a modern-day thriller. . . . To have crafted a page-turner out of the history of statistics is an impressive feat. If only lectures at university had been this racy."—David Robson, New Scientist"The Theory That Would Not Die is an impressively researched, rollicking tale of the triumph of a powerful mathematical tool."—Andrew Robinson, Nature Vol. 475"McGrayne is such a good writer that she makes this obscure battle gripping for the general reader."—Engineering and Technology Magazine"Scientists and statisticians have fought over a deep philosophical divide about probability, which Sharon Bertsch McGrayne explores with great clarity and wit."—Christine Evans-Pughe, Engineering and Technology Magazine"McGrayne holds the hand of the general reader as she lays out the history of the theorem and how it is now used in just about every walk of life. . . . Science writing at its absolute peak."—The BooksellerEditor's Choice, New York Times Book Review"We now know how to think rationally about our uncertain world. This book describes in vivid prose, accessible to the lay person, the development of Bayes' rule over more than two hundred years from an idea to its widespread acceptance in practice."—Dennis Lindley, University College London"A book simply highlighting the astonishing 200 year controversy over Bayesian analysis would have been highly welcome. This book does so much more, however, uncovering the almost secret role of Bayesian analysis in a stunning series of the most important developments of the twentieth century. What a revelation and what a delightful read!"—James Berger, Arts & Sciences Professor of Statistics, Duke University, and member, National Academy of Sciences"Well known in statistical circles, Bayes’s Theorem was first given in a posthumous paper by the English clergyman Thomas Bayes in the mid-eighteenth century. McGrayne provides a fascinating account of the modern use of this result in matters as diverse as cryptography, assurance, the investigation of the connection between smoking and cancer, RAND, the identification of the author of certain papers in The Federalist, election forecasting and the search for a missing H-bomb. The general reader will enjoy her easy style and the way in which she has successfully illustrated the use of a result of prime importance in scientific work."—Andrew I. Dale, author of A History of Inverse Probability From Thomas Bayes to Karl Pearson and Most Honorable Remembrance: The Life and Work of Thomas Bayes"Compelling, fast-paced reading full of lively characters and anecdotes . . . A great story."—Robert E. Kass, Carnegie Mellon University"Fascinating . . . I truly admire [McGrayne’s] style of writing, and . . . ability to turn complex mathematical ideas into intriguing stories, centered around real people."—Judea Pearl, winner of the 2012 Turing Award

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Book SynopsisIf you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

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Book SynopsisBayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasTrade Review"A refreshing and solid read for anyone confused or distracted by Bayesian recipe books."--Carsten F. Dormann, Quarterly Review of BiologyTable of ContentsPreface ix I Fundamentals 1 1 PREVIEW 3 1.1 A Line of Inference for Ecology 4 1.2 An Example Hierarchical Model 11 1.3 What Lies Ahead? 15 2 DETERMINISTIC MODELS 17 2.1 Modeling Styles in Ecology 17 2.2 A Few Good Functions 21 3 PRINCIPLES OF PROBABILITY 29 3.1 Why Bother with First Principles? 29 3.2 Rules of Probability 31 3.3 Factoring Joint Probabilities 36 3.4 Probability Distributions 39 4 LIKELIHOOD 71 4.1 Likelihood Functions 71 4.2 Likelihood Profiles 74 4.3 Maximum Likelihood 76 4.4 The Use of Prior Information in Maximum Likelihood 77 5 SIMPLE BAYESIAN MODELS 79 5.1 Bayes' Theorem 81 5.2 The Relationship between Likelihood and Bayes' 85 5.3 Finding the Posterior Distribution in Closed Form 86 5.4 More about Prior Distributions 90 6 HIERARCHICAL BAYESIAN MODELS 107 6.1 What Is a Hierarchical Model? 108 6.2 Example Hierarchical Models 109 6.3 When Are Observation and Process Variance Identifiable? 141 II Implementation 143 7 MARKOV CHAIN MONTE CARLO 145 7.1 Overview 145 7.2 How Does MCMC Work? 146 7.3 Specifics of the MCMC Algorithm 150 7.4 MCMC in Practice 177 8 INFERENCE FROM A SINGLE MODEL 181 8.1 Model Checking 181 8.2 Marginal Posterior Distributions 190 8.3 Derived Quantities 194 8.4 Predictions of Unobserved Quantities 196 8.5 Return to the Wildebeest 201 9 INFERENCE FROM MULTIPLE MODELS 209 9.1 Model Selection 210 9.2 Model Probabilities and Model Averaging 222 9.3 Which Method to Use? 227 III Practice in Model Building 231 10 WRITING BAYESIAN MODELS 233 10.1 A General Approach 233 10.2 An Example of Model Building: Aboveground Net Primary Production in Sagebrush Steppe 237 11 PROBLEMS 243 11.1 Fisher's Ticks 244 11.2 Light Limitation of Trees 245 11.3 Landscape Occupancy of Swiss Breeding Birds 246 11.4 Allometry of Savanna Trees 247 11.5 Movement of Seals in the North Atlantic 248 12 SOLUTIONS 251 12.1 Fisher's Ticks 251 12.2 Light Limitation of Trees 256 12.3 Landscape Occupancy of Swiss Breeding Birds 259 12.4 Allometry of Savanna Trees 264 12.5 Movement of Seals in the North Atlantic 268 Afterword 273 Acknowledgments 277 A Probability Distributions and Conjugate Priors 279 Bibliography 283 Index 293

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Book SynopsisWe live in the era of big data. However, small data sets are still common for ethical, financial, or practical reasons. Small sample sizes can cause researchers to seek out the most powerful methods to analyse their data, but they may also be wary that some methodologies and assumptions may not be appropriate when samples are small. The book offers advice on the statistical analysis of small data sets for various designs and levels of measurement, helping researchers to analyse such data sets, but also to evaluate and interpret others'' analyses. The book discusses the potential challenges associated with a small sample, as well as the ways in which these challenges can be mitigated. General topics with strong relevance to small sample sizes such as meta-analysis, sequential and adaptive designs, and multiple testing are introduced. While the focus is on hypothesis tests and confidence intervals, Bayesian analyses are also covered. Code written in the statistical software R is presente

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Book SynopsisAn introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis.This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.The book first covers elementary probability theory, the binomial model, the multinomial model, and methods for comparing dif

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Book SynopsisBayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous treTrade Review"The book has a practice-oriented, hands-on approach with R codes and outputs, clear examples, relevant exercises to elucidate the main concepts (with solutions included at the end). [...] Statisticians, data scientists and other researchers new to Bayesian networks might also find it valuable and interesting."-Anikó Lovik in ISCB News, June 2022Praise for the first edition:"… an excellent introduction to Bayesian networks with detailed user-friendly examples and computer-aided illustrations. I enjoyed reading Bayesian Networks: With Examples in R and think that the book will serve very well as an introductory textbook for graduate students, non-statisticians, and practitioners in Bayesian networks and the related areas."—Biometrics, September 2015"Several excellent books about learning and reasoning with Bayesian networks are available and Bayesian Networks: With Examples in R provides a useful addition to this list. The book is usually easy to read, rich in examples that are described in great detail, and also provides several exercises with solutions that can be valuable to students. The book also provides an introduction to topics that are not covered in detail in existing books … . It also provides a good list of search algorithms for learning Bayesian network structures. But the major strength of the book is the simplicity that makes it particularly suitable to students with sufficient background in probability and statistical theory, particularly Bayesian statistics."—Journal of the American Statistical Association, June 2015Table of Contents1. The Discrete Case: Multinomial Bayesian Networks. 2. The Discrete Case: Multinomial Bayesian Networks. 3. The Mixed Case: Conditional Gaussian Bayesian Networks. 4. Time Series: Dynamic Bayesian Networks. 5. More Complex Cases: General Bayesian Networks. 6. Theory and Algorithms for Bayesian Networks. 7. Software for Bayesian Networks. 8. Real-World Applications of Bayesian Networks.

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Book SynopsisPriced very competitively compared with other textbooks at this level!This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inferencestudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions develops notions of convergence in probability and distribution spotlights the central limit theorem (CLT) for the sample variance introduces sampling distributions and the Cornish-Fisher expansions concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity explaTrade Review"...the book contains unique features throughout. Examples are the moment problem, which is clarified through a nice example, the role of the probability generating functions, and the central limit theorem for the sample variance. Techniques and concepts are typically illustrated through a series of examples. Within a box is routinely summarized what it is that has been accomplished or where to go from that point. At the end of each chapter a long list of exercises is arranged according the sections. "---Zentralblatt fur Mathematik, 2000"…a marvelous book for students."-Statistical Papers "…a handy reference as well as a good textbook."-International Statistical Institute, Short Book Reviews Table of ContentsNotions of probability; expectations of functions of random variables; multivariate random variables; transformations and sampling distributions; notions of stochastic convergence; sufficiency, completeness and ancillarity; point estimation; tests of hypotheses; confidence interval estimation; Bayesian methods; likelihood ratio and other tests; large-sample inference; sample size determination - two-stage procedures. Appendices: abbreviations and notation; celebration of statistics - selected biographical notes; selected statistical tables.

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Book Synopsis Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

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Book SynopsisReviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. This book advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals.Trade Review"As the creator of bayesm (R software for Bayesian inference) and lead author of Bayesian Statistics and Marketing, Rossi has deep knowledge of the book's titular methods."--ChoiceTable of ContentsPreface vii 1 Mixtures of Normals 1 1.1 Finite Mixture of Normals Likelihood Function 6 1.2 Maximum Likelihood Estimation 9 1.3 Bayesian Inference for the Mixture of Normals Model 15 1.4 Priors and the Bayesian Model 16 1.5 Unconstrained Gibbs Sampler 25 1.6 Label-Switching 29 1.7 Examples 34 1.8 Clustering Observations 46 1.9 Marginalized Samplers 49 2 Dirichlet Process Prior and Density Estimation 59 2.1 Dirichlet Processes--A Construction 60 2.2 Finite and Infinite Mixture Models 64 2.3 Stick-Breaking Representation 68 2.4 Polya Urn Representation and Associated Gibbs Sampler 70 2.5 Priors on DP Parameters and Hyper-parameters 72 2.6 Gibbs Sampler for DP Models and Density Estimation 78 2.7 Scaling the Data 80 2.8 Density Estimation Examples 81 3 Non-parametric Regression 90 3.1 Joint vs. Conditional Density Approaches 90 3.2 Implementing the Joint Approach with Mixtures of Normals 93 3.3 Examples of Non-parametric Regression Using Joint Approach 96 3.4 Discrete Dependent Variables 104 3.5 An Example of Expenditure Function Estimation 108 4 Semi-parametric Approaches 115 4.1 Semi-parametric Regression with DP Priors 115 4.2 Semi-parametric IV Models 122 5 Random Coefficient Models 152 5.1 Introduction 152 5.2 Semi-parametric Random Coefficient Logit Models 157 5.3 An Empirical Example of a Semi-parametric Random Coefficient Logit Model 161 6 Conclusions and Directions for Future Research 187 6.1 When Are Non-parametric and Semi-parametric Methods Most Useful? 187 6.2 Semi-parametric or Non-parametric Methods? 189 6.3 Extensions 191 Bibliography 195 Index 201

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Book SynopsisBayesian statistical analyses have become increasingly common over the last two decades. The rapid increase in computing power that facilitated their implementation coincided with major changes in the research interests of, and data availability for, social scientists. Specifically, the last two decades have seen an increase in the availability of panel data sets, other hierarchically structured data sets including spatially organized data, along with interests in life course processes and the influence of context on individual behavior and outcomes. The Bayesian approach to statistics is well-suited for these types of data and research questions. Applied Bayesian Statistics is an introduction to these methods that is geared toward social scientists. Author Scott M. Lynch makes the material accessible by emphasizing application more than theory, explaining the math in a step-by-step fashion, and demonstrating the Bayesian approach in analyses of U.S. political trends drawing on data from the General Social Survey.Trade ReviewA lucid exposition of the Bayesian approach to statistics, accessible to those new to this approach. -- David GreenbergThe book′s presentation of the logic of the Bayesian approach is one of the better illustrations that I′ve encountered. The level of mathematical precision used here is technical, but the layout makes it approachable. -- Matthew PhillipsTable of Contents1. Introduction 2. Probability Distributions and Review of Classical Analysis 3. The Bayesian Approach to Probability and Statistics 4. Markov Chain Monte Carlo (MCMC) Sampling Methods 5. Implementing the Bayesian Approach in Realistic Applications 6. Conclusion

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Book SynopsisThis book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers track when observations are nonlinear functions of target site, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target.

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Book SynopsisThe volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.Table of ContentsAdaptive Sequential Posterior Simulators for Massively Parallel Computing Environments. Model Switching and Model Averaging in Time-Varying Parameter Regression Models. Assessing Bayesian Model Comparison in Small Samples. Bayesian Selection of Systemic Risk Networks. Parallel Constrained Hamiltonian Monte Carlo for BEKK Model Comparison. Factor Selection in Dynamic Hedge Fund Replication Models: A Bayesian Approach. Determining the Proper Specification for Endogenous Covariates in Discrete Data Settings. Variable Selection in Bayesian Models: Using Parameter Estimation and Non Parameter Estimation Methods. Intrinsic Priors for Objective Bayesian Model Selection. Copyright page. Bayesian Model Comparison. List of Contributors. Preface. Advances in Econometrics. Bayesian Model Comparison. Demand Estimation with High-Dimensional Product Characteristics. Copula Analysis of Correlated Counts.

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Book SynopsisThis book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning. Table of Contents1. Computer experiments.- 2. Surrogate models.- 3. Unconstrained optimization.- 4. Constrained optimization.

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Book SynopsisThese lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches. Table of ContentsUncertainty and Decisions.- Prior and Likelihood Representation.- Graphical Modeling.- Parametric Models.- Computational Inference.- Bayesian Software Packages.- Model choice.- Linear Models.- Nonparametric Models.- Nonparametric Regression.- Clustering and Latent Factor Models.- Conjugate Parametric Models.

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Book SynopsisThis open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.Table of ContentsIntroduction to Bayesian statistical inference.- Sampling from complex probability distributions: a Monte Carlo primer for engineers.- Introduction to the theory of imprecise probability.- Imprecise discrete-time Markov chains.- Statistics with imprecise probabilities – a short survey.- Reliability.- Simulation methods for the analysis of complex systems.- Overview of stochastic model updating in aerospace application under uncertainty treatment.- Aerospace flight modeling and experimental testing.

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Book SynopsisThis book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.Table of ContentsIntroduction.- Fundamentals. - Bayesian models for Dynamic Scene Analysis.- Multi-layer label fusion models.- Multitemporal data analysis with Marked Point Processes. - Multi-level object population analysis with an EMPP model.- Concluding Remarks.- References.- Index.

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Book SynopsisThis book provides an overview of the emerging topics in biostatistical theories and methods through their applications to evidence-based global health research and decision-making. It brings together some of the top scholars engaged in biostatistical method development on global health to highlight and describe recent advances in evidence-based global health applications. The volume is composed of five main parts: data harmonization and analysis; systematic review and statistical meta-analysis; spatial-temporal modeling and disease mapping; Bayesian statistical modeling; and statistical methods for longitudinal data or survival data. It is designed to be illuminating and valuable to both expert biostatisticians and to health researchers engaged in methodological applications in evidence-based global health research. It is particularly relevant to countries where global health research is being rigorously conducted.Table of Contents1. Harmonization of Longitudinal Population Data: evidence from three rural health and demographic surveillance system nodes in South Africa.- 2. Adjusting for Selection Bias in Assessing the Efficacy of Health Inputs on Birth Outcome: Evidence from South-Saharan Africa.- 3. An Indirect Assessment of the Effect of Anthropogenic Activities on the Ecology of the Intermediate Snail Host for Schistosoma Haematobium.- 4. Diagonal Reference Modeling of Effects of Couples' Educational Differences on Women's Health Care Utilization in Sub-Saharan Africa - Gebrenegus Ghilagaber, Michael Carlson.- 5. Sequential Modeling of Parity Progression Ratios in Sub-Saharan Africa.- 6. Evidence-informed Public Health, Systematic Reviews, and Meta-analysis.- 7. Meta-analysis Methods and Empirical Comparison of Aggregate Data and Individual Participant Data Results from Sample Survey Data.- 8. Statistical Meta-analysis and its Efficience Between Summary Statistics and Individual Participant-level Data: A Monte-Carlo simulation study.- 9. Multivariate Disease Mapping for Multiple Health Outcomes.- 10. Measuring Spatial Dependence of Non-communicable Diseases in South Africa.- 11. Mapping Health Outcomes in Sub-Saharan African Region Using Survey Data, Adjusting for Survey Data - Sheyla Rodrigues Cassy.- 12. Spatial Multi-criteria Decision Analysis in Health Sciences: Fifteen years of applications and trends.- 13. Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression.- 14. Dynamic Bayesian Adjustment of Educational Gradients in Divorce Risks: Disentangling causation and misclassification.- 15. Bayesian Dynamic Models for Time-Varying Outcomes: Applications to a patient cohort on ART.- 16. Suicide Ideation and Associated Factors Among School-going Adolescents in Namibia: A Multilevel logistic regression.- 17. Bayesian Inference in the Extended Generalized Gamma Model and its Special Cases: With applications on demographic and health survey data from Sub-Saharan Africa.- 18. Changing Effects of Covariates on Childhood Mortality in Sub-Saharan Africa: A dynamic Bayesian survival modeling approach.- 19. Group Outliers and Influence Assessments in Clustered Survival Data Modeling.- 20. Joint Modeling of Competing Risks Survival and Longitudinal Data

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Book SynopsisThis book hosts the results presented at the 6th Bayesian Young Statisticians Meeting 2022 in Montréal, Canada, held on June 22–23, titled "Bayesian Statistics, New Generations New Approaches". This collection features selected peer-reviewed contributions that showcase the vibrant and diverse research presented at meeting. This book is intended for a broad audience interested in statistics and aims at providing stimulating contributions to theoretical, methodological, and computational aspects of Bayesian statistics. The contributions highlight various topics in Bayesian statistics, presenting promising methodological approaches to address critical challenges across diverse applications. This compilation stands as a testament to the talent and potential within the j-ISBA community. This book is meant to serve as a catalyst for continued advancements in Bayesian methodology and its applications and encourages fruitful collaborations that push the boundaries of statistical research.Table of ContentsJ. Owen, I. Vernon, J. Carter, Bayesian Emulation of Complex Computer Models with Structured Partial Discontinuities.- B. Hansen, A. Avalos-Pacheco, M. Russo, Roberta De Vito, A Variational Bayes Approach to Factor Analysis. P. Strong, Jim Q. Smith, Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees.- G. Vasdekis, Gareth O. Roberts, Speeding up the Zig-Zag process.- V. Ghidini, S. Legramanti, R. Argiento, Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks.- A. Lachi, C. Viscardi, M. Baccini, Approximate Bayesian inference for smoking habit dynamics in Tuscany.

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Book Synopsis- Introduction.- Bayesian Spatial Modelling.- Conjugate Inversion Models.- Random Fields.- Part I Traditional Conjugate Spatial Models.- Likelihood Models.- Prior Models.- Posterior Models.- Model Parameter Inference.- Computational Challenges.

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Book SynopsisThis book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.Table of Contents-1. Introduction.- 2. Simple Random Sampling.- 3. Stratied Random Sampling.- 4. Cluster Sampling.- 5. Double Sampling.- 6. Probability Proportional to Size Sampling.- 7. Systematic Sampling.- 8. Resampling Techniques.- 9. Adaptive Cluster Sampling.- 10. Two-Stage Adaptive Cluster Sampling.- 11. Adaptive Cluster Double Sampling.- 12. Inverse Adaptive Cluster Sampling.- 13. Two Stage Inverse Adaptive Cluster Sampling.- 14. Stratified Inverse Adaptive Cluster Sampling.- 15. Negative Adaptive Cluster Sampling.- 16. Negative Adaptive Cluster Double Sampling.- 17. Two- Stage Negative Adaptive Cluster Sampling.- 18. Balanced and Unbalanced Ranked Set Sampling.- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference.- 20. Important Versions of Ranked Set Sampling.- 21. Sampling Errors.

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Book SynopsisThis book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.Table of Contents1. Basics of Time Series.- 2. Statistical Inference for Stationary Time Series.- 3. AR Models with Stationary Non-Gaussian Positive Marginals.- 4. AR Models with Stationary Non-Gaussian Real-Valued Marginals.- 5. Some Nonlinear AR-type Models for Non-Gaussian Time series.- 6. Linear Time Series Models with Non-Gaussian Innovations.- 7. Autoregressive-type Time Series of Counts.

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