2021 Book Award Winner, The Gospel Coalition (Public Theology & Current Events) Christians are often thought of as defending only their own religious interests in the public square. They are viewed as worrying exclusively about the erosion of their freedom to assemble and to follow their convictions, while not seeming as concerned about publicly defending the rights of Muslims, Hindus, Jews, and atheists to do the same. Andrew T. Walker, an emerging Southern Baptist public theologian, argues for a robust Christian ethic of religious liberty that helps the church defend religious freedom for everyone in a pluralistic society. Whether explicitly religious or not, says Walker, every person is striving to make sense of his or her life. The Christian foundations of religious freedom provide a framework for how Christians can navigate deep religious difference in a secular age. As we practice religious liberty for our neighbors, we can find civility and commonality amid disagreement, further the church's engagement in the public square, and become the strongest defenders of religious liberty for all. Foreword by noted Princeton scholar Robert P. George.

You're about to be introduced to the umami factor: the secret to sensational custom-made beverages, including spirits, wine, beer, soft drinks, and more. Chances are you have never heard of umami, the taste impression created by certain amino acids in a food or beverage. This book reveals the wonders of umami to beginner and expert alike, starting off with a thorough understanding of "full-spectrum" fermentation theory, then dives into the various preparation techniques and shows how umami-producing ingredients create beverages with sensational balance and roundness on the palate, tongue, nose, and even throat. More than 75 recipes, sharp insight, and handy tips help both amateur and professional fermentation chefs conquer the next frontier in beverage science.

First published in 2002. Routledge is an imprint of Taylor & Francis, an informa company.

First published in 2002. Routledge is an imprint of Taylor & Francis, an informa company.

Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation

There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage.

This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which coversapproximately 40% of the problems, is available for instructors who require the book for a course.

Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at UniversitA(c) Paris Dauphine, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books, including The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the SocietiA(c) de Statistique de Paris in 1995.

George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

This book makes the Business Associations or Corporations course accessible to students whether or not they already know anything about business. In a conversational tone, the original text and problems emphasize the legal issues that 99% of lawyers will encounter in their professional career. The book is organized around the life-cycle of a business. And while it includes landmark cases, the focus is on the legal issues encountered when starting a business, growing a business, and ending a business. This is the only current casebook for an introductory course in business associations that is co-authored by a business school professor. The book thus consistently and clearly provides students with the business context for understanding the legal issues explored.

We have sold 4300 copies worldwide of the first edition (1999).

This new edition contains five completely new chapters covering new developments.

Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here.

This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.