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Statistical Data Analysis

Author: Glen Cowan
Publisher: Oxford University Press
ISBN: 0198501560
Size: 59.40 MB
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This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

Data Analysis

Author: D. S. Sivia
Publisher: Oxford University Press
ISBN: 9780198518891
Size: 67.46 MB
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Statistics lectures have often been viewed with trepidation by engineering and science students taking an ancillary course in this subject. Whereas there are many texts showing "how" statistical methods are applied, few provide a clear explanation for non-statisticians of how the principlesof data analysis can be based on probability theory. Data Analysis: A Bayesian Tutorial provides such a text, putting emphasis as much on understanding "why" and "when" certain statistical procedures should be used as "how". This difference in approach makes the text ideal as a tutorial guide forsenior undergraduates and research students, in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. With its central emphasis on a fewfundamental rules, this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures.

Searches For Dijet Resonances

Author: Lydia Audrey Beresford
Publisher: Springer
ISBN: 331997520X
Size: 11.79 MB
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This book addresses one of the most intriguing mysteries of our universe: the nature of dark matter. The results presented here mark a significant and substantial contribution to the search for new physics, in particular for new particles that couple to dark matter. The first analysis presented is a search for heavy new particles that decay into pairs of hadronic jets (dijets). This pioneering analysis explores unprecedented dijet invariant masses, reaching nearly 7 TeV, and sets constraints on several important new physics models. The two subsequent analyses focus on the difficult low dijet mass region, down to 200 GeV, and employ a novel technique to efficiently gather low-mass dijet events. The results of these analyses transcend the long-standing constraints on dark matter mediator particles set by several existing experiments.

Error Analysis With Applications In Engineering

Author: Zbigniew A. Kotulski
Publisher: Springer Science & Business Media
ISBN: 9048135702
Size: 49.74 MB
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Our intention in preparing this book was to present in as simple a manner as possible those branches of error analysis which ?nd direct applications in solving various problems in engineering practice. The main reason for writing this text was the lack of such an approach in existing books dealing with the error calculus. Most of books are devoted to mathematical statistics and to probability theory. The range of applications is usually limited to the problems of general statistics and to the analysis of errors in various measuring techniques. Much less attention is paid in these books to two-dimensional and three-dim- sional distributions, and almost no attention is given to problems connected with the two-dimensional and three-dimensional vectorial functions of independent random variables. The theory of such vectorial functions ?nds new applications connected, for example, with analysis of the positioning accuracy of various mechanisms, among them of robot manipulators and automatically controlled earth-moving and loading machines, such as excavators.

Practical Methods For Reliability Data Analysis

Author: J. I. Ansell
Publisher: Oxford University Press
ISBN: 9780198536642
Size: 65.91 MB
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This is a practical text for those who wish to analyse data from Reliability studies. The emphasis is on clear explanation of the techniques used, supported by extensive mathematical and statistical background and nature of the data before it is analysed. There are chapters on survivalanalysis, using illuminating case studies.

Data Analysis

Author: Devinderjit Sivia
Publisher: Oxford University Press
ISBN: 0198568312
Size: 19.92 MB
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Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design. The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.

Aspects Of Multivariate Statistical Analysis In Geology

Author: E. Savazzi
Publisher: Elsevier
ISBN: 9780080527611
Size: 58.12 MB
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The book presents multivariate statistical methods useful in geological analysis. The essential distinction between multivariate analysis as applied to full-space data (measurements on lengths, heights, breadths etc.) and compositional data is emphasized with particular reference to geochemical data. Each of the methods is accompanied by a practically oriented computer program and backed up by appropriate examples. The computer programs are provided on a compact disk together with trial data-sets and examples of the output. An important feature of this book is the graphical system developed by Dr. Savazzi which is entitled Graph Server. Geological data often deviate from ideal statistical requirements. For this reason, close attention has been paid to the analysis of data that contain atypical observations.

Chemometrics In Environmental Analysis

Author: Jürgen W. Einax
Publisher: John Wiley & Sons
ISBN: 9783527287727
Size: 41.96 MB
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J. W. Einax, H. W. Zwanziger S. Gei Chemometrics in Environmental Analysis Make the most of your data! This new title will serve both as an introduction and as a practical guide to those techniques of chemometrics which are applicable to environmental analysis. By describing the optimum methods of data analysis it will help all chemists in this field to save time and money. Because the authors demonstrate the most important chemometric methods with the aid of numerous examples, the reader will learn to solve a given problem by use of the appropriate method. Applications range from sampling, through laboratory analysis, to evaluation. Interpretation of the findings is explained clearly. The text covers not only basic methods such as univariate statistics, regression analysis, and statistical test planning, but also multivariate data analysis, for example, cluster analysis, principal components analysis, and factor and discriminant analysis. Case studies show the enormous possibilities, and the limits, of chemometric methods. The book will help all environmental analytical scientists, even those with only a basic knowledge of mathematics, to optimize the evaluation and interpretation of the results of their measurements.

Probabilistic Graphical Models For Genetics Genomics And Postgenomics

Author: Christine Sinoquet
Publisher: OUP Oxford
ISBN: 0191019208
Size: 60.24 MB
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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.

Statistical Models In Epidemiology

Author: David Clayton
Publisher: OUP Oxford
ISBN: 0191650919
Size: 54.70 MB
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This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.