Books about Computation from Amazon.com

This highly anticipated revision builds upon the strengths of the previous edition. Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter..
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There has been a dramatic growth in the development and application of Bayesian inferential methods Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.

This book is a suitable companion book for an introductory course on Bayesian methods. Also the book is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book.

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Susanna Epp's DISCRETE MATHEMATICS, THIRD EDITION provides a clear introduction to discrete mathematics. Renowned for her lucid, accessible prose, Epp explains complex, abstract concepts with clarity and precision. This book presents not only the major themes of discrete mathematics, but also the reasoning that underlies mathematical thought. Students develop the ability to think abstractly as they study the ideas of logic and proof. While learning about such concepts as logic circuits and computer addition, algorithm analysis, recursive thinking, computability, automata, cryptography, and combinatorics, students discover that the ideas of discrete mathematics underlie and are essential to the science and technology of the computer age. Overall, Epp's emphasis on reasoning provides students with a strong foundation for computer science and upper-level mathematics courses..
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Revised and updated, the third edition of Golub and Van Loan's classic text in computer science provides essential information about the mathematical background and algorithmic skills required for the production of numerical software. This new edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of the modified Gram-Schmidt process, and new material devoted to GMRES, QMR, and other methods designed to handle the sparse unsymmetric linear system problem.

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Presents the knowledge necessary for an appreciation of the wide scope of applications for discrete-time signal processing and a foundation for contributing to future developments in this technology. DLC: Signal processing--Mathematics. .
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This long-awaited work from one of the world's most respected scientists presents a series of dramatic discoveries never before made public. Starting from a collection of simple computer experiments---illustrated in the book by striking computer graphics---Wolfram shows how their unexpected results force a whole new way of looking at the operation of our universe.

Wolfram uses his approach to tackle a remarkable array of fundamental problems in science: from the origin of the Second Law of thermodynamics, to the development of complexity in biology, the computational limitations of mathematics, the possibility of a truly fundamental theory of physics, and the interplay between free will and determinism.

Written with exceptional clarity, and illustrated by more than a thousand original pictures, this seminal book allows scientists and non-scientists alike to participate in what promises to be a major intellectual revolution..
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What do cars, toys, people, and jelly beans have in common? They can all be estimated. Two friends try out their estimating skills and find out that estimating can have real rewards––especially when there’s a contest to enter!

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Designed for non-expert student, enthusiast, or researcher, this text provides an accessible introduction to numerical computation and its applications in science and engineering. It assumes no prior knowledge beyond undergraduate calculus and elementary computer programming. Fundamental and practical issues are discussed in a unified manner with a generous, but not excessive, dose of numerical analysis. Topics are introduced on a need to know basis to concisely illustrate the practical implementation of a variety of algorithms and demystify seemingly esoteric numerical methods. Algorithms that can be explained without too much elaboration and can be implemented within a few dozen lines of computer code are discussed in detail, and computer programs in Fortran, C++, and Matlab are provided. Algorithms whose underlying theories require long, elaborate explanations are discussed at the level of first principles, and references for further information are given. The book uses numerous schematic illustrations to demonstrate concepts and facilitate their understanding by providing readers with a helpful interplay between ideas and visual images. Real-world examples drawn from various branches of science and engineering are presented. Updated information on computer technology and numerical methods is included, many new and some original topics are introduced. Additional solved and unsolved problems are included..
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This classic book on formal languages, automata theory, and computational complexity has been updated to present theoretical concepts in a concise and straightforward manner with the increase of hands-on, practical applications. This new edition comes with Gradiance, an online assessment tool developed for computer science.

 

Gradiance is the most advanced online assessment tool developed for the computer science discipline.  With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students.  By using a series of “root questions” and hints, it not only tests a student’s capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material.  Through the programming labs, instructors are capable of testing, tracking, and honing their students’ skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered. 

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