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Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
This is the eBook version of the printed book. Read the following excerpt from Rethink, Chapter 5: Make (and Break) Connections. When you rethink your company and study it using the “what” as your unit of analysis, you can see that, at its core, it is a tightly woven fabric of connections--emotional, financial, technical--that cut across organizational boundaries. Pull on one thread and many others might move. The clerk in accounting is related to the chief dispatcher who went to school with the director of advertising. A glitch in production or a spurt in sales can send shock waves from one end of the company to the other, from design to delivery, and it might well affect the bottom line. So before you act upon your knowledge of which “whats” are high in value and low in performance, before you set in motion a process-improvement program, you need to examine the connections between and among your target “whats.” A failure to do so can wreak havoc with a company’s profits and prospects. Dell Inc. learned that lesson the hard way. Dell was launched in 1984 by a young entrepreneur with a brilliant strategy. He would sell made-to-order computers directly to customers, primarily businesses, without benefit of retail outlets. The brick-and-mortar middlemen were charging too much, Michael Dell concluded, and giving customers ridiculously inadequate technical support to boot. He intended to sidestep both pitfalls. In particular, his company was going to provide outstanding tech support. And it did that famously, until the day it didn’t, infamously. In the early 2000s, in pursuit of lower overhead, Dell began to outsource the resolve Customer-Questions/Problems“what.” To continue reading, purchase and download now.
Informed consent is a central topic in contemporary biomedical ethics. Yet attempts to set defensible and feasible standards for consenting have led to persistent difficulties. In Rethinking Informed Consent in Bioethics, first published in 2007, Neil Manson and Onora O'Neill set debates about informed consent in medicine and research in a fresh light. They show why informed consent cannot be fully specific or fully explicit, and why more specific consent is not always ethically better. They argue that consent needs distinctive communicative transactions, by which other obligations, prohibitions, and rights can be waived or set aside in controlled and specific ways. Their book offers a coherent, wide-ranging and practical account of the role of consent in biomedicine which will be valuable to readers working in a range of areas in bioethics, medicine and law.
To date, much of the empirical work in social epidemiology has demonstrated the existence of health inequalities along a number of axes of social differentiation. However, this research, in isolation, will not inform effective solutions to health inequalities. Rethinking Social Epidemiology provides an expanded vision of social epidemiology as a science of change, one that seeks to better address key questions related to both the causes of social inequalities in health (problem-focused research) as well as the implementation of interventions to alleviate conditions of marginalization and poverty (solution-focused research). This book is ideally suited for emerging and practicing social epidemiologists as well as graduate students and health professionals in related disciplines.
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Rethinking Europe's Future is a major reevaluation of Europe's prospects as it enters the twenty-first century. David Calleo has written a book worthy of the complexity and grandeur of the challenges Europe now faces. Summoning the insights of history, political economy, and philosophy, he explains why Europe was for a long time the world's greatest problem and how the Cold War's bipolar partition brought stability of a sort. Without the Cold War, Europe risks revisiting its more traditional history. With so many contingent factors--in particular Russia and Europe's Muslim neighbors--no one, Calleo believes, can pretend to predict the future with assurance. Calleo's book ponders how to think about this future. The book begins by considering the rival ''lessons'' and trends that emerge from Europe's deeper past. It goes on to discuss the theories for managing the traditional state system, the transition from autocratic states to communitarian nation states, the enduring strength of nation states, and their uneasy relationship with capitalism. Calleo next focuses on the Cold War's dynamic legacies for Europe--an Atlantic Alliance, a European Union, and a global economy. These three systems now compete to define the future. The book's third and major section examines how Europe has tried to meet the present challenges of Russian weakness and German reunification. Succeeding chapters focus on Maastricht and the Euro, on the impact of globalization on Europeanization, and on the EU's unfinished business--expanding into ''Pan Europe,'' adapting a hybrid constitution, and creating a new security system. Calleo presents three models of a new Europe--each proposing a different relationship with the U.S. and Russia. A final chapter probes how a strong European Union might affect the world and the prospects for American hegemony. This is a beautifully written book that offers rich insight into a critical moment in our history, whose outcome will shape the world long after our time.
An overview of allostasis, the process by which the body maintains overall viability under normal and adverse conditions.
A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.