February 12, 2018 by CRC Press
Reference - 412 Pages - 7 B/W Illustrations
ISBN 9781138588035
An Introduction to Integral Transforms is meant for students pursuing graduate and post graduate studies in Science and Engineering. It contains discussions on almost all transforms for normal users of the subject. The content of the book is explained from a rudimentary stand point to an advanced level for convenience of its readers. Pre]requisite for understanding the subject matter of the book is some knowledge on the complex variable techniques.
Please note: Taylor & Francis does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.
1. Fourier Transform, 2. Finite Fourier Transform, 3. The Laplace Transform, 4. The Inverse Laplace Transform and Application, 5. Hilbert and Stieltjes Transforms, 6. Hankel Transforms, 7. Finite Hankel Transforms, 8. The Mellin Transform, 9. Finite Laplace Transforms, 10. Legendre Transforms, 11. The Kontorovich-Lebedev Transform, 12. The Mehler-Fock Transform, 13. Jacobi, Gegenbauer, Laguerre and Hermite Transforms, 14. The Z-Transform
April 10, 2018
Reference - 200 Pages - 372 Color Illustrations
ISBN 9781138301153 - CAT# K35708
Discusses the authors personal experience of the Hyperbolic Plane
Includes some more recent applications of hyperbolic geometry such as medicine, neuroscience,
architecture, fashion, design, quantum computing, networks
Richly illustrated with photographs and colored illustrations
Winner, Euler Book Prize, awarded by the Mathematical Association of America. With over 200 full color photographs, this non-traditional, tactile introduction to non-Euclidean geometries also covers early development of geometry and connections between geometry, art, nature, and sciences. For the crafter or would-be crafter, there are detailed instructions for how to crochet various geometric models and how to use them in explorations. New to the 2nd Edition; Daina Taimina discusses her own adventures with the hyperbolic planes as well as the experiences of some of her readers. Includes recent applications of hyperbolic geometry such as medicine, architecture, fashion & quantum computing.
Foreword by William Thurston
Introduction
1. What Is the Hyperbolic Plane? Can We Crochet It?
2. What Can You Learn from Your Model?
3. Four Strands in the History of Geometry
4. Tidbits from the History of Crochet
5. What is Non-Euclidean Geometry?
6. Pseudosphere
7. Metamorphoses of the Hyperbolic Plane
8. Other Surfaces with Negative Curvature
9. Looking for Applications of Hyperbolic Geometry
10. Hyperbolic Crochet goes Viral
Appendix. How to Make Models
April 16, 2018
Textbook - 376 Pages - 73 B/W Illustrations
ISBN 9781138741683
ISBN 9781138741515 - paperback
Series: Chapman & Hall/CRC Texts in Statistical Science
An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them
Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis
Connects Bayesian analysis and MCMC methods to fit GLMs
Contains numerous examples from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods
Offers the data sets and solutions to the exercises online
Describes the components of good statistical practice to improve scientific validity and reproducibility of results.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Introduction
Model Fitting
Exponential Family and Generalized
Linear Models
Estimation
Inference
Normal Linear Models
Binary Variables and Logistic Regression
Nominal and Ordinal Logistic Regression
Poisson Regression and Log-Linear Models
Survival Analysis
Clustered and Longitudinal Data
Bayesian Analysis
Markov Chain Monte Carlo Methods
Example Bayesian Analyses
Postface
Appendix
September 1, 2018
Textbook - 256 Pages
ISBN 9781138627673
Series: Series in Materials Science and Engineering
* Introduces the basic concepts of geometry and topology in the context of soft matter physics.
* Offers a unified approach, balancing mathematical rigor and explaining fundamentals in a precise, clear way that elucidates understanding of the behavior of real materials.
* Presents real examples such as packing, topological insulators, thermal fluctuations, etc.
* Written for experimentalists and theoreticians alike.
* Includes exercises to enhance comprehension.
This book provides an introduction to the geometrical and topological methods that have become essential tools for soft condensed matter physicists. The emphasis is on providing mathematical tools in clear but precise language, focusing on fundamental concepts using real examples to illustrate for readers in vivid, three-dimensional fashion. The motivation is clearly provided using problems from soft condensed matter, from the packing of colloids to liquid crystals and thermal fluctuations of lipid membranes.
Introduction. Symmetry and Groups. Geometry of Curves in the Plane. Curves in Space. Geometry of Surfaces. Geometry of Surfaces and Fluid Interfaces. Elasticity and Riemannian Geometry. Topology and Topological Defects. Geometric Frustration. More Advanced Topology and Geometry
September 4, 2018 Forthcoming
Textbook - 600 Pages - 1 Color & 45 B/W Illustrations
ISBN 9781138197015 - CAT# K31232
Series: Textbooks in Mathematics
First introduced in 1995, Cryptography: Theory and Practice is the most widely used and referenced textbook for introductory cryptography courses taught throughout the world. Through three editions this textbook has been embraced by instructors and students alike.
The fourth edition provides mathematical background in a "just-in-time" fashion, informal descriptions of cryptosystems along with more precise pseudocode, and a host of numerical examples and exercises. The authors offer comprehensive, in-depth treatment of the methods and protocols that are vital to safeguarding the seemingly infinite and increasing amount of information circulating around the world.
Introduction to Cryptography. Classical Cryptography. Shannon's Theory, Perfect Secrecy and the One-Time Pad. Block Ciphers and Stream Ciphers. Hash Functions and Message Authentication. The RSA Cryptosystem and Factoring Integers. Public-Key Cryptography and Discrete Logarithms. Post-quantum Cryptography. Identification Schemes and Entity Authentication. Key Distribution. Key Agreement Schemes. Miscellaneous Topics. Appendix A: Number Theory and Algebraic Concepts for Cryptography, Appendix B: Pseudorandom Bit Generation for Cryptography.
August 2, 2018 Forthcoming
Reference - 278 Pages
ISBN 9781138303867 1
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Estimation of large dispersion and autocovariance matrices using banding and tapering
Joint convergence of high dimensional generalized dispersion matrices
Limiting spectral distribution of symmetric polynomials in sample autocovariance matrices and normality of traces
Application of free probability in high dimensional time series
Estimation of coefficient matrices in high dimensional autoregressive process
Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis and basic results in stochastic convergence.
Part I is on different methods of estimation of large covariance matrices and auto-covariance matrices and properties of these estimators. Part II covers the relevant material on random matrix theory and non-commutative probability. Part III provides results on limit spectra and asymptotic normality of traces of symmetric matrix polynomial functions of sample auto-covariance matrices in high-dimensional linear time series models. These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series.
The book should be of interest to people in econometrics and statistics (large covariance matrices and high-dimensional time series), mathematics (random matrices and free probability) and computer science (wireless communication). Parts of it can be used in post-graduate courses on high-dimensional statistical inference, high-dimensional random matrices and high-dimensional time series models. It should be particularly attractive to researchers developing statistical methods in high-dimensional time series models.
Estimation of large covariance matrices and large autocovariance matrices. Estimation of large covariance matrices for i.i.d. observations. Estimation of large covariance matrices for dependent observations. Estimation of large autocovariance matrices for linear processes. Spectral distribution, free probability and limits of generalized covariance matrices. Essentials of limiting spectral distribution and free probability. Joint convergence of generalized matrices when p=n ! y 2 (0;1). Joint convergence of generalized covariance matrices when p=n ! 0. Large dimensional sample autocovariance matrices and their traces, with statistical application. Limit spectra of sample autocovariance matrices. Trace of polynomials in sample autocovariance matrices. Inference in multiple high dimensional time series.