This assessment instrument was over 10 years in the making. During most of that time I was traveling the United States on seminar tours where I had the amazing opportunity to train thousands of colleagues in optimal diagnosis and treatment of Obsessive Compulsive Spectrum disorders. Occasionally we would have people with OCD in the seminar. Often the professional in the audience were not aware of how unique it was to have someone with OCD teaching how to treat OCD. The attendees with OCD got it touch and, though I would often get standing ovations, their applause at the end of the day was always the most warm and vigorous. They knew that I "got it." While I have great respect for my fellow mental health professionals who attempt to understand and assist people with OCD, the reality is you just can't fully get it unless you are one of us. There are a number of paper and pencil instruments designed to gather data from people with OCD to augment treatment planning and progress, the most common among them the Yale-Brown Obsessive Compulsive Scale. This special assessment instrument was developed from the point of view of the person with OCD. As such it focuses on collecting information that will lead to one very specific goal - assisting the person completing the survey in feeling better My goal in its construction was to elicit the kind of information which OCD sufferers have told me they want their treatment provides to have. In a sense taking a test, survey or inventory is participating in research - generating data. I believe all healthcare research must demonstrate the ability to improve the human condition or it should not be sanctioned. Hopefully The OCD Recovery Center Comprehensive Inventory accomplishes that. The OCDRC-CI is designed in sections so that separate assessments can be made of the range of symptoms the individual is experiencing, the intensity of those symptoms, the conditions which might work for or against them in the healing process, and the presence of Special Characteristics as well as co-existing obsessive compulsive spectrum problems. Your feedback as a user is always welcome so feel free to be in touch. I hope the OCDRC-CI will be helpful for you and those you share it with.

This book provides an update of synthetic information in marine sedimentology, associating generalities to case studies. New information is replaced in a context of plate tectonics and evolution of ocean systems at geological scale. Besides general information, the book insists on the relationships between plate tectonics and sedimentation, as well as on the formation and evolution of sediment series and their potential as archives of past environments.
* complete update of synthetic information in marine sedimentology.
* association of information on the origin and transport of sediment particles, the evolution of sediment series and their role as archives of past environments.

This graduate-level textbook, now in paperback, presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory and some modern and advanced topics of Bayesian statistics.

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 graduate-level textbook presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration, including Gibbs sampling and other MCMC techniques. The second edition includes a new chapter on model choice (Chapter 7) and the chapter on Bayesian calculations (6) has been extensively revised. Chapter 4 includes a new section on dynamic models. In Chapter 3, the material on noninformative priors has been expanded, and Chapter 10 has been supplemented with more examples. The Bayesian Choice will be suitable as a text for courses on Bayesian analysis, decision theory or a combination of them. Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at the Université Paris Dauphine, and external lecturer at Ecole Polytechnique, Palaiseau, France. He was previously 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. In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written three other books, including Monte Carlo Statistical Method (Springer 1999) with George Casella. He also edited Discretization and MCMC Convergence Assessment (Springer 1998). He has served or is serving as an associate editor for the Annals of Statistics, the Journal of the American Statistical Association, Statistical Science, and Sankhya. He is a fellow of the Institute of Mathematical Statistics, and the Young Statistician Award of the Société de Statistique de Paris in 1995.

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.