Add to Wish List. Close Preview. Toggle navigation Additional Book Information. Summary An intuitive and mathematical introduction to subjective probability and Bayesian statistics. Both rigorous and friendly, the book contains: Introductory chapters examining each new concept or assumption Just-in-time mathematics — the presentation of ideas just before they are applied Summary and exercises at the end of each chapter Discussion of maximization of expected utility The basics of Markov Chain Monte Carlo computing techniques Problems involving more than one decision-maker Written in an appealing, inviting style, and packed with interesting examples, Principles of Uncertainty introduces the most compelling parts of mathematics, computing, and philosophy as they bear on statistics.

Author s Bio Joseph B. Reviews "… it is a book about Bayesian probability, statistics, and decision making.

## CRC Press Online - Series: Chapman & Hall/CRC Texts in Statistical Science

Liski, International Statistical Review , In this remarkable book, Kadane begins at the most rudimentary level, develops all the needed mathematics on the fly, and still manages to flesh out at least the core of the whole story, slowly, thoughtfully, and rigorously, right up to graduate level. Request an e-inspection copy. Share this Title. Related Titles. Bayesian Methods for Data Analysis. Theoretical Statistics.

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### 2010 – today

Designed to show students how to work with a set of "real world data," Miller's text goes beyond any specific discipline, and considers a whole variety of techniques from ANOVA…. By Thomas S. A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard….

By David J. Hand , Martin J. This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it…. By Peter Guttorp. Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner.

The distinguishing feature of this work…. By Henry C. This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering. The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. By Chris Chatfield. This book illuminates the complex process of problem solving, including formulating the problem, collecting and analyzing data, and presenting the conclusions.

## Principles of Uncertainty

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear…. By D Bissell. Statistical Methods for SPC and TQM sets out to fill the gap for those in statistical process control SPC and total quality management TQM who need a practical guide to the logical basis of data presentation, control charting, and capability indices.

Statistical theory is introduced in a…. By Martin J. Crowder , Alan Kimber , T. Sweeting , R. Written for those who have taken a first course in statistical methods, this book takes a modern, computer-oriented approach to describe the statistical techniques used for the assessment of reliability.

By Bent Jorgensen.

This book provides a self-contained exposition of the theory of linear models, including practical aspects of residuals and data analysis. By Bernard Lindgren. This classic textbook is suitable for a first course in the theory of statistics for students with a background in calculus, multivariate calculus, and the elements of matrix algebra.

Many scientists and technologists would like to carry out their own statistical analyses without reference to a professional statistician. Often, however, they have no knowledge of statistics or otherwise do not know how to apply it to research and development problems. The first edition of…. Snell , H. GENSTAT is a general purpose statistical computing system with a flexible command language operating on a variety of data structures.

It may be used on a number of computer ranges, either interactively for exploratory data analysis, or in batch mode for standard data analysis. The great flexibility…. Clark This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book.

Kadane An intuitive and mathematical introduction to subjective probability and Bayesian statistics. Polansky Helping students develop a good understanding of asymptotic theory, Introduction to Statistical Limit Theory provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. Introduction to General and Generalized Linear Models 1st Edition By Henrik Madsen , Poul Thyregod Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data.

O'Brien Drawn from nearly four decades of Lawrence L. Time Series Modeling, Computation, and Inference, 1st Edition By Raquel Prado , Mike West Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis.

Trosset Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Morgan Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications.

Louis Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Monahan A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance ANOVA.

Introduction to Probability with R 1st Edition By Kenneth Baclawski Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view.

Time Series Analysis 1st Edition By Henrik Madsen With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. DeMets Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Smeeton While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.

Matthews Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Lopes While there have been few theoretical contributions on the Markov Chain Monte Carlo MCMC methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds.

Gotway Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. Jewell Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Shou Lin Ziqiu Yun. Mohammad Ahsanullah. Semiconductor-based Sensors. Solar Cell Device Physics. Stephen J.

Microbial Food Safety An Introduction. Mehmet T.

Bruce M. Symmetry A Mathematical Exploration. Ming He Toh-Ming Lu. Principles of Polymer Chemistry. Agricultural Biotechnology in China Origins and Prospects. Valerie J. Karplus Xing Wang Deng. Maurice E. Chemical Proteomics Methods and Protocols. Odontogenesis Methods and Protocols.

Zebrafish Protocols for Neurobehavioral Research Neuromethods.