Introduction to Computational Modeling Using C and Open-Source Tools presents the fundamental principles of computational models from a computer science perspective. It explains how to implement these models using the C programming language. The software tools used in the book include the Gnu Scientific Library (GSL), which...
Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs:...
Understanding how cancer tumours develop and spread is vital for finding treatments and cures. Cancer Modelling and Simulation demonstrates how mathematical modelling and computer simulation techniques are used to discover and gain insight into the dynamics of tumour development and growth. It highlights the benefits of tumour modelling, such...
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical...
Unlike in the related area of bioinformatics, few books currently exist that document the techniques, tools, and algorithms of chemoinformatics. Bringing together worldwide experts in the field, the Handbook of Chemoinformatics Algorithms provides an overview of the most common chemoinformatics algorithms in a single...
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such...
Stephen Chapman's Essentials of MATLAB Programming is a successful freshman-level text that is useable in a wide range of courses. This brief text serves two purposes -- it teaches how to program using MATLAB as a technical programming language as well as teaching students the basics of computer programming. Using top-down design...
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)âclassification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is...
Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB®, Second Edition uses numerous examples and applications to...
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis...
The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability....
Address Errors before Users Find Them
Using a mix-and-match approach, Software Test Attacks to Break Mobile and Embedded Devices presents an attack basis for testing mobile and embedded systems. Designed for testers working in the ever-expanding world of "smart" devices driven by software, the book...
