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Books in Statistics and probability

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    • Statistical Aspects of Water Quality Monitoring

      • 1st Edition
      • Volume 27
      • September 22, 2011
      • A.H. El-Shaarawi + 1 more
      • English
      • eBook
        9 7 8 0 0 8 0 8 7 0 1 8 2
      This volume contains selected papers from the ``Workshop on the Statistical Aspects of Water Quality Monitoring'', held on October 7-10 1985, at the National Water Research Institute in Burlington, Ontario, Canada. The prime objective of the Workshop was to generate interaction between the statistical community and scientists working in the area of Water Quality Monitoring. To this end, topics covered in this Workshop fall into two categories: (1) Methods Development, and (2) the Imaginative Application of Existing Methodologies. Subjects covered include: Time Series, Estimation of Loading, Clustering, Model Development, Censoring Data Analysis, Quality Control and Data Acquisition.In the area of environmental sciences, statistical applications are still in their infancy, with few attempts to systematically develop techniques dealing with environmental issues. The publication of this book is one step towards identifying appropriate statistical techniques and diagnosing problems in Water Quality Monitoring which require new statistical methodologies. The papers presented in this volume represent international expertise, consolidating detailed information on both conventional and new methods.

    • Probabilities and Potential, B

      • 1st Edition
      • Volume 72
      • August 18, 2011
      • C. Dellacherie + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 4 5 5 7 6 3 6
      • eBook
        9 7 8 0 0 8 0 8 7 1 8 3 7

    • Probabilities and Potential, C

      • 1st Edition
      • Volume 151
      • August 18, 2011
      • C. Dellacherie + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 4 5 5 7 0 7 0
      • eBook
        9 7 8 0 0 8 0 8 7 2 6 2 9
      This third volume of the monograph examines potential theory. The first chapter develops potential theory with respect to a single kernel (or discrete time semigroup). All the essential ideas of the theory are presented: excessive functions, reductions, sweeping, maximum principle. The second chapter begins with a study of the notion of reduction in the most general situation possible - the ``gambling house'' of Dubins and Savage. The beautiful results presented have never been made accessible to a wide public. These are then connected with the theory of sweeping with respect to a cone of continuous functions, and the integral representation in compact convex sets. The third chapter presents new or little-known results, with the aim of illustrating the effectiveness of capacitary methods in the most varied fields. The last two chapters are concerned with the theory of resolvents.The fourth and last part of the English edition will be devoted to the theory of Markov processes.

    • Statistical Mechanics

      • 3rd Edition
      • February 28, 2011
      • Paul D. Beale
      • English
      • eBook
        9 7 8 0 1 2 3 8 2 1 8 9 8
      Statistical Mechanics explores the physical properties of matter based on the dynamic behavior of its microscopic constituents. After a historical introduction, this book presents chapters about thermodynamics, ensemble theory, simple gases theory, Ideal Bose and Fermi systems, statistical mechanics of interacting systems, phase transitions, and computer simulations. This edition includes new topics such as BoseEinstein condensation and degenerate Fermi gas behavior in ultracold atomic gases and chemical equilibrium. It also explains the correlation functions and scattering; fluctuationdissipati... theorem and the dynamical structure factor; phase equilibrium and the Clausius-Clapeyron equation; and exact solutions of one-dimensional fluid models and two-dimensional Ising model on a finite lattice. New topics can be found in the appendices, including finite-size scaling behavior of Bose-Einstein condensates, a summary of thermodynamic assemblies and associated statistical ensembles, and pseudorandom number generators. Other chapters are dedicated to two new topics, the thermodynamics of the early universe and the Monte Carlo and molecular dynamics simulations. This book is invaluable to students and practitioners interested in statistical mechanics and physics.

    • Exploring Monte Carlo Methods

      • 1st Edition
      • April 6, 2011
      • William L. Dunn + 1 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 1 5 7 5 9
      • Paperback
        9 7 8 0 4 4 4 5 5 8 6 4 0
      • eBook
        9 7 8 0 0 8 0 9 3 0 6 1 9
      Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon’s needle problem" provides a unifying theme as it is repeatedly used to illustrate many features of Monte Carlo methods. This book provides the basic detail necessary to learn how to apply Monte Carlo methods and thus should be useful as a text book for undergraduate or graduate courses in numerical methods. It is written so that interested readers with only an understanding of calculus and differential equations can learn Monte Carlo on their own. Coverage of topics such as variance reduction, pseudo-random number generation, Markov chain Monte Carlo, inverse Monte Carlo, and linear operator equations will make the book useful even to experienced Monte Carlo practitioners.

    • Statistical Methods in the Atmospheric Sciences

      • 3rd Edition
      • Volume 100
      • May 20, 2011
      • Daniel S. Wilks
      • English
      • Hardback
        9 7 8 0 1 2 3 8 5 0 2 2 5
      • eBook
        9 7 8 0 1 2 3 8 5 0 2 3 2
      Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines.

    • Practical Business Statistics

      • 6th Edition
      • January 25, 2011
      • Andrew F. Siegel
      • English
      • Paperback
        9 7 8 0 1 2 8 1 0 1 8 1 0
      • Hardback
        9 7 8 0 1 2 3 8 5 2 0 8 3
      • eBook
        9 7 8 0 1 2 3 8 5 2 0 9 0
      Practical Business Statistics, Sixth Edition, is a conceptual , realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize, mathematical correctness. The book offers a deep understanding of how to learn from data and how to deal with uncertainty while promoting the use of practical computer applications. This teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results. The text uses excellent examples with real world data relating to the functional areas within Business such as finance, accounting, and marketing. It is well written and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details. This edition features many examples and problems that have been updated with more recent data sets, and continues to use the ever-changing Internet as a data source. Supplemental materials include companion website with datasets and software. Each chapter begins with an overview, showing why the subject is important to business, and ends with a comprehensive summary, with key words, questions, problems, database exercises, projects, and cases in most chapters. This text is written for the introductory business/management statistics course offered for undergraduate students or Quantitative Methods in Management/ Analytics for Managers at the MBA level.

    • Doing Bayesian Data Analysis

      • 1st Edition
      • October 27, 2010
      • John Kruschke
      • English
      • Hardback
        9 7 8 0 1 2 3 8 1 4 8 5 2
      • eBook
        9 7 8 0 1 2 3 8 1 4 8 6 9
      There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.

Related subjects

Probability theory and stochastic processes

Statistics