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Probability and Statistics
The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. The full text downloaded to your c...
Student Solutions Manual for Probability and Statistics
This manual contains completely worked-out solutions for all the odd-numbered exercises in the text.
A Modern Introduction to Probability and Statistics
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Statistics in the Law
Introduction: Deciding Whether to be an Expert Witness 6. Part 1. What's it like to be an Expert Witness? 9. Introduction. A: Pioneers. 1. Damned Liars and Expert Witnesses Paul Meier. 2. Statisticians, Econometricians, and Adversary Proceedings Franklin M. Fisher. B A Very Brief Introduction to U.S. Law, and to the Role of Expert Witnesses. C Qualifications and Responsibilities of the Expert Witness 33. 1. Epidemiologic Evidence in the Silicone Breast Implant Cases Michael O. Finkelstein and Bruce Levin. 2. Frye v. United States. 3. Daubert v. Merrell Dow Pharmaceuticals. 4. Kumho Tire Co. v.
Probability and Statistics
The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.
All of Statistics
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Expected Utility Hypotheses and the Allais Paradox
Utility theory or, value theory in general, is certainly the cornerstone of decision theory, game theory, microecon~mics, and all social and political theories which deal with public decisions. Recently the American School of utility, founded by von N eumann Morgenstern, encountered a far-going criticism by the French School of utility represented by its founder Allais. The whole basis of the theory of decisions involving risk has been shaken and put into question. Consequently, basic research in the fundamentals of utility and value theory evolved into a crisis. Like any crisis in basic research, and this one was not an exception, it was very fruitful. One may simply say: Allais versus von Neumann-Morgenstern, or the French School of utility versus the American School, became one of the battlefields of scientific development which proved to be a most creative source of new advances and new developments in all those sciences which are based on evaluation of utilities.
Probability
Preface -- Combinatorics -- Probability -- Expectation values -- Distributions -- Gaussian approximations -- Correlation and regression -- Appendices.
The Bomb
Before the Bomb, there were simply 'bombs', lower case. But it was the twentieth century, one hundred years of almost incredible scientific progress, that saw the birth of the Bomb, the human race's most powerful and most destructive discovery. In this magisterial and enthralling account, Gerard DeGroot gives us the life story of the Bomb, from its birth in the turn-of-the-century physics labs of Europe to a childhood in the New Mexico desert of the 1940s, from adolescence and early adulthood in Nagasaki and Bikini, Australia and Siberia to unsettling maturity in test sites and missile silos all over the globe. By turns horrific, awe-inspiring and blackly comic, The Bomb is never less than compelling.
Bayesian Theory
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics
