Showing 97–120 of 158 results
More than ever before, complicated mathematical procedures are integral to the success and advancement of technology, engineering, and even industrial production. Knowledge of and experience with these procedures is therefore vital to present and future scientists, engineers and technologists.
This is a history of the use of Bayes theoremfrom its discovery by Thomas Bayes to the rise of the statistical competitors in the first part of the twentieth century. The book focuses particularly on the development of one of the fundamental aspects of Bayesian statistics, and in this new edition readers will find new sections on contributors to the theory. In addition, this edition includes amplified discussion of relevant work.
Modern complex large-scale dynamical systems exist in virtually every aspect of science and engineering, and are associated with a wide variety of physical, technological, environmental, and social phenomena, including aerospace, power, communications, and network systems, to name just a few. This book develops a general stability analysis and control design framework for nonlinear large-scale interconnected dynamical systems, and presents the most complete treatment on vector Lyapunov function methods, vector dissipativity theory, and decentralized control architectures.
“Elementary Statistics: A Step By Step Approach” is for introductory statistics courses with a basic algebra prerequisite. The book is non-theoretical, explaining concepts intuitively and teaching problem solving through worked examples and step-by-step instructions. In recent editions, Al Bluman has placed more emphasis on conceptual understanding and understanding results, along with increased focus on Excel, MINITAB, and the TI-83 Plus and TI-84 Plus graphing calculators; computing technologies commonly used in such courses.
Blow-up for Higher-Order Parabolic, Hyperbolic, Dispersion and Schrödinger Equations shows how four types of higher-order nonlinear evolution partial differential equations (PDEs) have many commonalities through their special quasilinear degenerate representations. The authors present a unified approach to deal with these quasilinear PDEs.The book first studies the particular self-similar singularity solutions (patterns) of the equations. This approach allows four different classes of nonlinear PDEs to be treated simultaneously to establish their striking common features.
This book provides an introduction to hypergraphs, its aim being to overcome the lack of recent manuscripts on this theory. In the literature hypergraphs have many other names such as set systems and families of sets. This work presents the theory of hypergraphs in its most original aspects, while also introducing and assessing the latest concepts on hypergraphs. The variety of topics, their originality and novelty are intended to help readers better understand the hypergraphs in all their diversity in order to perceive their value and power as mathematical tools.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy.
The mathematical theory of Krylov subspace methods with a focus on solving systems of linear algebraic equations is given a detailed treatment in this principles-based book. Starting from the idea of projections, Krylov subspace methods are characterised by their orthogonality and minimisation properties. Projections onto highly nonlinear Krylov subspaces can be linked with the underlying problem of moments, and therefore Krylov subspace methods can be viewed as matching moments model reduction.
‘SALT’ provides a step-by-step approach to the most useful statistical analyses for language test developers and researchers based on the programs IBM SPSS, Winsteps and Facets. Each chapter focuses on one particular type of analysis, for example, analysing how items in a test are performing or investigating the relationship between two variables. Each chapter begins with an introduction as to why this particular analysis is important for the language tester and then provides explanations about the terms and concepts which the reader will meet in the chapter.
The method for carrying out each analysis is then described in a systematic manner guiding the reader through the procedure for that particular analysis. The main aspects of the output files are then investigated and the results explained. Eleven of the book’s appendices provide further opportunities for the reader to repeat many of the statistical procedures on different data sets. Questions guide the reader in checking their understanding of the statistical procedures and output tables.
The geometry of Hessian structures is a fascinating emerging field of research. It is in particular a very close relative of Kahlerian geometry, and connected with many important pure mathematical branches such as affine differential geometry, homogeneous spaces and cohomology.
Wolfgang Doeblin, one of the greatest probabilists of this century, died in action during World War II at the age of twenty-five. He left behind several seminal contributions which have profoundly influenced the field and continue to provide inspiration for current research. This book is based on papers presented at the conference, ‘Fifty Years after Doeblin: Developments in the Theory of Markov Chains, Markov Processes, and Sums of Random Variables’, held at Blaubeuren, Germany, in November 1991.
This book is the first comprehensive introduction to smooth ergodic theory. It consists of two parts: the first introduces the core of the theory and the second discusses more advanced topics. In particular, the book describes the general theory of Lyapunov exponents and its applications to the stability theory of differential equations, the concept of nonuniform hyperbolicity, stable manifold theory (with emphasis on the absolute continuity of invariant foliations), and the ergodic theory of dynamical systems with nonzero Lyapunov exponents.
Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.
Despite the seemingly close connections between mathematics and other scientific and engineering fields, practical explanations intelligible to those who are not primarily mathematicians are even more difficult to find. The Dictionary of Applied Mathematics for Engineers and Scientists fills that void.
This volume presents a detailed description of the statistical distributions that are commonly applied to such fields as engineering, business, economics and the behavioural, biological and environmental sciences. The authors cover specific distributions, including logistic, slash, bathtub, F, non-central Chi-square, quadratic form, non-central F, non-central t, and other miscellaneous distributions.
This book provides a self-contained comprehensive exposition of the theory of dynamical systems. The book begins with a discussion of several elementary but crucial examples. These are used to formulate a program for the general study of asymptotic properties and to introduce the principal theoretical concepts and methods. The main theme of the second part of the book is the interplay between local analysis near individual orbits and the global complexity of the orbit structure. The third and fourth parts develop the theories of low-dimensional dynamical systems and hyperbolic dynamical systems in depth.
curvilinear coordinates. This treatment includes in particular a direct proof of the three-dimensional Korn inequality in curvilinear coordinates. The fourth and last chapter, which heavily relies on Chapter 2, begins by a detailed description of the nonlinear and linear equations proposed by W.T. Koiter for modeling thin elastic shells.
The Handbook of Linear Algebra provides comprehensive coverage of linear algebra concepts, applications, and computational software packages in an easy-to-use handbook format. The esteemed international contributors guide you from the very elementary aspects of the subject to the frontiers of current research.
Bayesian Theory José M. Bernardo Universidad de Valencia, Valencia, Spain Adrian F. M. Smith Imperial College of Science, Technology and Medicine, London, UK Bayesian Theory is the first volume of a related series of three and will be followed by Bayesian Computation, and Bayesian Methods. The series aims to provide an up–to–date overview of the why?, how? and what? of Bayesian statistics. This volume provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory.
Many fundamentally important decisions about our social life are a function of how well we understand and analyze DATA. This sounds so obvious but it is so misunderstood. Social statisticians struggle with this problem in their teaching constantly. This book and its approach is the ally and support of all instructors who want to accomplish this hugely important teaching goal.This innovative text for undergraduate social statistics courses is, (as one satisfied instructor put it), a "breath of fresh air.&
Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results.Features numerous examples using actual engineering and scientific studies.Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.Deep and concentrated experimental design coverage, with equivalent but separate emphasis on the analysis of data from the various designs.Topics can be implemented by practitioners and do not require a high level of training in statistics.N
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures
Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.
The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data.
The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
Using and Understanding Mathematics: A Quantitative Reasoning Approach prepares students for the mathematics they will encounter in college courses, their future career, and life in general. Its quantitative reasoning approach helps students to build the skills needed to understand major issues in everyday life, and compels students to acquire the problem-solving tools that they will need to think critically about quantitative issues in contemporary society.This program will provide a better teaching and learning experience–for you and your students.
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.See What’s New in the Second Edition:Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.D
Showing 97–120 of 158 results