Showing 1–24 of 75 results
This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statistical mechanics, and related fields. After briefly recalling essential background in statistical mechanics and probability theory, it gives a succinct overview of simple sampling methods. The concepts behind the simulation algorithms are explained comprehensively, as are the techniques for efficient evaluation of system configurations generated by simulation.
This is a comprehensive survey on the research on the parabolic Anderson model – the heat equation with random potential or the random walk in random potential – of the years 1990 – 2015. The investigation of this model requires a combination of tools from probability (large deviations, extreme-value theory, e.g.) and analysis (spectral theory for the Laplace operator with potential, variational analysis, e.g.). We explain the background, the applications, the questions and the connections with other models and formulate the most relevant results on the long-time behavior of the solution, like quenched and annealed asymptotics for the total mass, intermittency, confinement and concentration properties and mass flow.
Learn all of Excel’s statistical toolsTest your hypotheses and draw conclusionsUse Excel to give meaning to your dataUse Excel to interpret statsStatistical analysis with Excel is incredibly useful—and this book shows you that it can be easy, too! You’ll discover how to use Excel’s perfectly designed tools to analyze and understand data, predict trends, make decisions, and more. Tackle the technical aspects of Excel and start using them to interpret your data!Inside…Covers Excel 2016 for Windows® & Mac® usersCheck out new Excel stuffMake sense of worksheetsCreate shortcutsTool around with analysisUse Quick StatisticsGraph your dataWork with probabilityHandle random variables
Authors: Davey, S., Gordon, N., Holland, I., Rutten, M., Williams, J.Presents a unique insight into the Bayesian calculation of the search zone for MH370Written by members of the MH370 search teamContains a tutorial description of available data allowing readers to work out their own solutionsThis book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated.
"Theory of Numbers: A Textbook" is aimed at students of Mathematics who are graduates or even undergraduates. Very little prerequisites are needed. The reader is expected to know the theory of functions of a real variable and in some chapters complex integration and some simple principles of complex function theory are assumed. The entire book is self contained except theorems 7 and 9 of chapter 11 which are assumed. The most ambitious chapter is chapter 11 where the most attractive result on difference between consecutive primes is proved.
Stochastic geometry involves the study of random geometric structures, and blends geometric, probabilistic, and statistical methods to provide powerful techniques for modeling and analysis. Recent developments in computational statistical analysis, particularly Markov chain Monte Carlo, have enormously extended the range of feasible applications. Stochastic Geometry: Likelihood and Computation provides a coordinated collection of chapters on important aspects of the rapidly developing field of stochastic geometry, including:o a "crash-course" introduction to key stochastic geometry themeso considerations of geometric sampling bias issueso tesselationso shapeo random setso image analysiso spectacular advances in likelihood-based inference now available to stochastic geometry through the techniques of Markov chain Monte Carlo
Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.Divided into six parts, the handbook begins by establishing notation and terminology.
This book provides a guide to businesses on how to use analytics to help drive from ideas to execution. Analytics used in this way provides “full lifecycle support” for business and helps during all stages of management decision-making and execution.
The framework presented in the book enables the effective interplay of business, analytics, and information technology (business intelligence) both to leverage analytics for competitive advantage and to embed the use of business analytics into the business culture.
This textbook aims to fill the gap between those that offer a theoretical treatment without many applications and those that present and apply formulas without appropriately deriving them. The balance achieved will give readers a fundamental understanding of key financial ideas and tools that form the basis for building realistic models, including those that may become proprietary. Numerous carefully chosen examples and exercises reinforce the student’s conceptual understanding and facility with applications.
Authors: Celona, JohnNumerous step-by-step tutorials help the reader to learn quicklyProblems included at the end of each chapterIncludes complete examples of how to structure most common legal questionsThis book is the first in-depth guide to applying the philosophy, theory, and methods of decision analysis to creating and executing winning legal strategies. With explanations that progress from introductory to advanced and practice problems at the end of each chapter, this is a book the reader will want to use and refer to for years to come.
Authors: Fortuna, Luigi, Nunnari, Giuseppe, Nunnari, SilviaShows the researcher and practising engineer how to use time-series forecasting to help integrate intermittent sources of renewable power into the gridTeaches students the essentials of applied time-series analysisProvides the reader with software package and data set that helps put the methods described into practiceThis brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.—
How to make simple sense of complex statistics–from the author of Numbers Rule Your World
We live in a world of Big Data–and it’s getting biggerevery day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it–whether we realize it or not.
Where do you send your child for the best education?Big Data. Which airline should you choose to ensure a timely arrival? Big Data. Who will you vote for in the next election? Big Data.
The problem is, the more data we have, the more difficult it is to interpret it.
Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors’ views on interpretation have evolved. The changes to Stata and to the authors’ views inspired the authors to completely rewrite their popular SPost commands to take advantage of the power of the margins command and the flexibility of factor-variable notation.
A culmination of the author’s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.
Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments.
The application of engineering principles in divergent fields such as management science and communications as well as the advancement of several approaches in theory and computation have led to growing interest in queueing models, creating the need for a comprehensive text. Emphasizing Markovian structures and the techniques that occur in different models, A Course on Queueing Models discusses recent developments in the field, different methodological tools – some of which are not available elsewhere – and computational techniques.W
An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practiceProviding accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms.A
Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.T
What could be better than the bestselling Schaum’s Outline series? For students looking for a quick nuts-and-bolts overview, it would have to be Schaum’s Easy Outline series. Every book in this series is a pared-down, simplified, and tightly focused version of its predecessor.
Scientists and engineers must use methods of probability to predict the outcome of experiments, extrapolate results from a small case to a larger one, and design systems that will perform optimally when the exact characteristics of the inputs are unknown.
Sponsored by the American Educational Research Association’s Special Interest Group for Educational Statisticians This volume is the second edition of Hancock and Mueller’s highly-successful 2006 volume, with all of the original chapters updated as well as four new chapters. The second edition, like the first, is intended to serve as a didactically-oriented resource for graduate students and research professionals, covering a broad range of advanced topics often not discussed in introductory courses on structural equation modeling (Sem).
STATISTICS FOR BUSINESS AND ECONOMICS brings together more than twenty-five years of author experience, sound statistical methodology, a proven problem-scenario approach, and meaningful applications to demonstrate how statistical information. Discover how the most trusted approach to statistics today is Simply Powerful with the latest market-leading text from respected authors Anderson/Sweeney/Williams. STATISTICS FOR BUSINESS AND ECONOMICS, 11e introduces sound statistical methodology within a strong applications setting.
Explores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer ScienceA flexible, interactive teaching format enhanced by a large selection of examples and exercisesDeveloped from the author’s own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science.After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes’ theorem, and Chebyshev inequality.
The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a "lay of the land" for latent variable mixture models before the volume moves to more specific constellations of topics.
Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methodsFeaturing a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up–to–date coverage of the unique challenges presented in the field of data analysis.T
Showing 1–24 of 75 results