Sauer discusses the fundamental concepts of numerical analysis: convergence, complexity, conditioning, compression, and orthogonality. Throughout, Spotlight features comment on each of these concepts as they are addressed, and make connections to other expressions of the same principle elsewhere in the book. The popular Reality Check in each chapter gives concrete, relevant examples of the way numerical methods lead to solutions of important scienti c and technological problems; they can be used to launch individual or team projects. MATLAB® is used throughout to demonstrate and implement numerical methods. 013469645X / 9780134696454 Numerical Analysis, 3/e

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As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement.

The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.

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Introduction to Statistics Using R is organized into 13 major chapters.

Each chapter is broken down into many digestible subsections in order to explore the objectives of the book. There are many real-life practical examples in this book and each of the examples is written in R codes to acquaint the readers with some statistical methods while simultaneously learning R scripts.

]]>Illustrates the power of linear algebra through practical applications This acclaimed theorem-proof text presents a careful treatment of the principal topics of linear algebra. It emphasizes the symbiotic relationship between linear transformations and matrices, but states theorems in the more general infinite-dimensional case where appropriate. Applications to such areas as differential equations, economics, geometry, and physics appear throughout, and can be included at the instructor's discretion.

0134860241 / 9780134860244 Linear Algebra, 5/e ]]>

This O'Reilly cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly--without having to comb through all the details of R's graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project and includes a discussion of how and why the recipe works.

Most of the recipes in this second edition use the updated version of the ggplot2 package, a powerful and flexible way to make graphs in R. You'll also find expanded content about the visual design of graphics. If you have at least a basic understanding of the R language, you're ready to get started with this easy-to-use reference.

Use R's default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and more Provide annotations to help viewers interpret data Control the overall appearance of graphics Explore options for using colors in plots Create network graphs, heat maps, and 3D scatter plots Get your data into shape using packages from the tidyverse]]>