July 15, 2017
Reference - 250 Pages - 75 B/W Illustrations
ISBN 9781482243772 - CAT# K23449
Series: Chapman & Hall/CRC Texts in Statistical Science
Provides a comprehensive practical guide to understanding, using, and interpreting the results of spatial random effects models in a concise stand-alone reference text
Includes simulation studies, both as a pedagogic tool and to demonstrate the strengths and weaknesses of the methods
Demonstrates fitting a wide range of models through examples that contain clear and user-friendly computer code
Gives instructions on downloading spatial data, reading spatial data in various formats into R, and performing any required data manipulations
This book provides a comprehensive reference for solving scientific problems with the generalized linear geostatistical model (GLGM), with an emphasis on demonstrating the accompanying software through examples. The key features of the GLGM are observed data points being independent of each other conditional on an unobserved spatial surface, values of the underlying spatial surface follow a multivariate normal distribution, and the surface is the sum of explanatory variables (fixed effects) and a random term.
July 15, 2017
Reference - 304 Pages - 50 B/W Illustrations
ISBN 9781498700443
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Offers an overview of asymptotic analysis of mixed effects models
Addresses linear mixed, generalized linear mixed, nonlinear mixed effects, and nonparametric models
Includes illustrations throughout, using real data examples
Covers all necessary theoretical details
Provides pointers to future research directions
Mixed effects models, including linear mixed models, generalized linear mixed models, nonlinear mixed effects models, and non-parametric mixed effects models, are complex models by nature; yet, these models are extensively used in practice. This monograph provides a comprehensive overview of asymptotic analysis of mixed effects models. The book has a good balance of theory and applications, with real data examples provided to motivate the theoretical development. It provides a summary of research in this field with pointers to future research directions.
Asymptotic Analysis of Linear Mixed Models. Asymptotic Theory for Generalized Linear Mixed Models. Small Area Estimation. Other Types of Mixed Models. Appendix.
June 26, 2017 Forthcoming by CRC Press
Reference - 320 Pages - 100 B/W Illustrations
ISBN 9781439847824
Provides a thorough overview of fundamentals, tools, and techniques
Includes a wide range of applications in the physical sciences
Offers images, links, and more on a CD-ROM and supporting website
Taking a very practical, hand-on approach, this book introduces the use of Monte Carlo methods for solving practical problems in experimental physics. Topics covered include simulation methods, simulation domains, the experimental lifecycle, software tools for simulation, event generators, generalized and specialized codes for particle transport, modeling techniques, and physics processes. A CD-ROM and supporting website feature downloads, codes, hints, images, and links that enhance the bookfs use in formal teaching environments and allow more experienced users access to further resources.
Introduction
CONCEPTS
Modeling
Simulation Methods
Simulation Domains
Simulation in the Experimental Lifecycle
TOOLS
Simulation Software
Event Generators
General-Purpose Codes for Particle Transport
Specialized Codes for Particle Transport
METHODS AND TECHNIQUES
The Software Process
Modeling the Experimental Environment
Physics Processes
Fast Simulation Techniques
Interacting With the Simulation
Reliable Simulation
Simulation Production
APPLICATION
A Basic Simulation Application in Detail
Conclusion
How to Learn More
Bibliography
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June 15, 2017 Forthcoming by CRC Press
Reference - 500 Pages - 20 B/W Illustrations
ISBN 9781482246346
Focuses on continuous rather than discrete variable systems
Presents mathematics in an accessible manner for readers from a physics or engineering background
Integrates both theoretical and experimental approaches
Offers a pedagogical, student-friendly style, which also is suitable for researchers
This book introduces the reader to the vast area of research focusing on quantum mechanical systems described by continuous variables, such as positions and momenta of particles, which provide the theoretical framework for quantum optics and, more generally, quantum field theory. Once acquainted with the material in the book, a student with a physics or engineering background will be able to tackle the literature in the field and undertake first-hand research.
Introduction. Preliminaries. Mathematical description of continuous variable systems. Quantum correlations of continuous variable systems. Bosonic communication channels. Quantum control of continuous variables. A gallery of experimental platforms. Proof of Williamson theorem. Fourier-Weyl relationship. Generators of the symplectic group.
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July 1, 2017
Reference - 200 Pages - 30 B/W Illustrations
ISBN 9781498776738
Explains MCMC methods and applications at an elementary level
Shows the differences between classical and Bayesian approaches
Offers many applied examples
This book presents a narrative account of Markov Chain Monte Carlo at a popular level. The author relies on history to provide a unifying narrative thread; on the centuries-old tension between gclassicalh and Bayesian approaches, as a plot theme that can highlight the importance of MCMC in changing the practice of statistics; and on applied examples and on metaphor in the hope of conveying the concepts without making technical demands of the reader.
An enduring controversy. The story of Bernoullifs Law of Averages. The story of Bayesfs theorem. The triumphs of Laplace and Gauss. The triumphs of Fisher and Neyman. The provocations of Brinbaum, Savage, and de Finetti. The computational obstruction. A computational breakthrough. The bomb, the computer, and the origins of Monte Carlo methods. Metropolis and Hastings. The Gemans; Gelfand and Smith. The Gibbs sampler. Hierarchical models. Statistics: No definitive answers; only evolving questions. Subjective or objective? The role of priors. Equal ignorance and the paradox of flat priors. Jeffries and the attempt at gobjective priors." Reanalysis and sensitivity analysis.