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Introduces the statistical techniques most commonly employed in physical sciences and engineering
Makes clear distinction between material that is strictly mathematical and theoretical, and practical applications of statistical methods
Expanded to cover selected core statistical methods used in business science
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains.
Features new to this edition include:
• a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets.
• a new chapter on the various measures of the mean including logarithmic averages.
• new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors.
• a new case study and additional worked examples.
• mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text.
• end-of-chapter summary boxes, for easy reference.
As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
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Number of Pages
XVII, 318
Number of Illustrations and Tables
36 b/w illustrations, 4 illustrations in colour
Topics
Mathematical Methods in Physics
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Statistics for Business, Economics, Mathematical Finance, Insurance
Appl. Mathematics / Computational Methods of Engineering
Complex Systems
Statistical Physics and Dynamical Systems
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Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation
Author(s)/Editor(s): Carl Graham and Denis Talay | Size: 2.07 MB | Format:PDF | Quality:Unspecified | Publisher: SPRINGER | Year: 2013 | pages: 264 | ISBN: ISBN: 3642393624
In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners’ aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems.
The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of Itô integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view.
The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations.
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This book reviews all the major research accomplishments and summarizes the different applications of radon. It serves as a solid reference book for researchers who are interested in the U-series radionuclides and noble gases as tracers and chronometers. Radon has been widely utilized as a powerful tracer to quantify a number of processes that include gas exchange rates between air and water, submarine groundwater discharge in coastal waters, water exchange between rivers and lakes, ocean circulation, hydrocarbon and uranium exploration. It is also used as an atmospheric tracer for the identification and quantification of air masses and as a tool for earthquake prediction, etc. A significant portion of the book presents state-of-the knowledge on indoor-radon-related health issues. Applications of the decay-series of Rn-222 are presented in a chapter. It serves as a reference and a state-of-the-art resource for researchers who want to learn the different applications of radon in Earth systems.
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The Oxford Brookes Department of Architecture exhibition explodes with colour and zeal, each presentation as unique as the area of study it represents’. Thus began the review of the 2009 end of year show in the Architect’s Journal, the first of two in the national architectural press.
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BRIDGE FOR DESIGN is the only high end interior design magazine that is directly delivered to accredited interior designers, hotel specifiers and architects internationally.
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This book presents a unified approach for modeling hydrologic processes distributed in space and time using geographic information systems (GIS). This Third Edition focuses on the principles of implementing a distributed model using geospatial data to simulate hydrologic processes in urban, rural and peri-urban watersheds. The author describes fully distributed representations of hydrologic processes, where physics is the basis for modeling, and geospatial data forms the cornerstone of parameter and process representation. A physics-based approach involves conservation laws that govern the movement of water, ranging from precipitation over a river basin to flow in a river.
Global geospatial data have become readily available in GIS format, and a modeling approach that can utilize this data for hydrology offers numerous possibilities. GIS data formats, spatial interpolation and resolution have important effects on the hydrologic simulation of the major hydrologic components of a watershed, and the book provides examples illustrating how to represent a watershed with spatially distributed data along with the many pitfalls inherent in such an undertaking. Since the First and Second Editions, software development and applications have created a richer set of examples, and a deeper understanding of how to perform distributed hydrologic analysis and prediction. This Third Edition describes the development of geospatial data for use in Vflo® physics-based distributed modeling.
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