Kategoria BISAC: Computers >> Mathematical & Statistical Software

Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers a comprehensive and up-to-date treatment of modern methods in matrix computation. It uses a unified approach to direct and iterative methods for linear systems, least squares and eigenvalue problems. A thorough analysis of the stability, accuracy, and complexity of the treated methods is given.

Numerical Methods in Matrix Computations is suitable for use in courses on scientific computing and applied technical areas at advanced...

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.

New to the Third Edition

• Three new chapters on quantile regression, missing values, and Bayesian inference
• Extra material in the logistic regression chapter that describes a regression model for ordered...

Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems. Applications covered in this book include optimization, finance, statistical mechanics, birth and death processes, and gambling systems.

Explorations in Monte Carlo Methods provides a hands-on approach to learning this subject. Each new idea is carefully motivated by a realistic problem, thus leading from questions to theory via examples and numerical simulations. Programming...

This textbook guides the reader through all stages of process optimization, from the investigation and presentation of empirical prior data to the planning and analysis of independent statistical experimental designs. It focuses on the proven methods relevant to practice and avoids excessively technical scientific descriptions. Practical methodology is presented in context.

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science...

The matrix laboratory interactive computing environment-MATLAB-has brought creativity to research in diverse disciplines, particularly in designing and programming experiments. More commonly used in mathematics and the sciences, it also lends itself to a variety of applications across the field of psychology. For the novice looking to use it in experimental psychology research, though, becoming familiar with MATLAB can be a daunting task.

MATLAB for Psychologists expertly guides readers through the component steps, skills, and operations of the software, with plentiful...

The theme of the meeting was "Statistical Methods for the Analysis of Large Data-Sets." In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event.

The book includes 45 papers from a selection of the 156 papers accepted for presentation...

This book discusses questions of numerical solutions of applied problems on parallel computing systems. Nowadays, engineering and scientific computations are carried out on parallel computing systems, which provide parallel data processing on a few computing nodes. In constructing computational algorithms, mathematical problems are separated in relatively independent subproblems in order to solve them on a single computing node.