Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Statistics: A Bayesian Perspective is a general introductory test that only assumes familiarity with college algebra and offers the following significant features : it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the scientific process, it develops ideas through uncommonly interesting and real-world examples, it introduces, early on, ideas of data analysis and experimental design, and it includes a data disk that also contains Minitab macros specifically useful for calculations.

Bayesian statistical decision theory --- Bayesian statistical decision theory. --- Statistics. --- Bayes' solution --- Bayesian analysis --- Statistical decision

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics.

Bayesian statistical decision theory --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Bayesian statistical decision theory. --- Mathematical statistics. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Recent books in the Wiley Series in Probability and Mathematical Statistics Editors Vic Barnett J. Stuart Hunter Adrian F.M. Smith Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Stephen M. Stigler Nicholas I. Fisher David G. Kendall Jozef L. Teugels Optimal Design of Experiments Friedrich Pukelsheim, Universita;t Augsburg, Augsburg, Germany Optimal Design of Experiments presents the first complete theoretical development of optimal design for the linear model, a unified exposition that embraces a wide variety of design problems. It describes the statistical theory involved in designing experiments, and applies it to typical special cases. The design problems originating from statistics are solved using tools from linear algebra and convex analysis. The material is presented in a very clear, careful and organized way. Rather than assaulting traditional ways of thinking about optimal design, this book pulls together formerly separate entities to create a common framework for diverse design problems that share a common goal. Statisticians, mathematicians, engineers, and operations research specialists will find this book stimulating, challenging, and an asset to their work. 1993 Statistics for Spatial Data, Revised Edition Noel Cressie, Iowa State University, USA Designed for the scientific and engineering professional eager to exploit its enormous potential, Statistics for Spatial Data is a primer to the theory as well as the nuts-and-bolts of this influential technique. Focusing on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, and reveals how spatial statistical models can be used to solve a host of problems in science and engineering. The previous edition was hailed by Mathematical Reviews as "an excellent book which…will become a basic reference". Revised to reflect state-of-the-art developments, this edition also features many detailed examples, numerous illustra

Bayesian statistical decision theory. --- Statistique bayésienne --- Bayesian statistical decision theory --- Bayes' solution --- Bayesian analysis --- Mathematical statistics --- Congresses --- Statistical decision --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Bayesian statistical decision theory - Congresses

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"A defense of the rationality of adductive inference from the criticisms of Bayesian theorists"--

Abduction (Logic) --- Reasoning. --- Practical reason. --- Bayesian statistical decision theory. --- PHILOSOPHY / Logic --- PHILOSOPHY / General --- SCIENCE / Cognitive Science --- Statistical decision --- Bayes' solution --- Bayesian analysis --- Logic --- Reasoning --- Syllogism --- Reason --- Practical rationality --- Practical reasoning --- Rationality, Practical --- Reasoning, Practical --- Thought and thinking --- Judgment (Logic) --- Argumentation --- Ratiocination

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematical statistics --- Bayesian statistical decision theory --- 681.3*I20 --- 681.3*I28 --- Bayesian statistical decision theory. --- 519.542 --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Artificial intelligence (AI) in general; cognitive simulation; philosophical foundations --- Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- 681.3*I28 Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- 681.3*I20 Artificial intelligence (AI) in general; cognitive simulation; philosophical foundations