Mathematics and statistics with the free software SCILAB (http://www-rocq.inria.fr/scilab/)

The book has been designed for Science, Engineering, Mathematics and Statistics undergraduate students. A look at the contents of the book will give the reader a clear idea of the variety of numerical methods discussed and analysed. The book has been written in a concise and lucid style with proper explanation of Mathematics involved in each method. Each method is explained with solved examples, computer programs and their results as a screenshot of the graphic window and console window. The careful organisation of figures, solved examples, codes, graphic window and console window help the students grasp quickly.

Familiarize yourself with Scilab using this concise, practical tutorial that is focused on writing code to learn concepts. Starting from the basics, this book covers array-based computing, plotting, and working with files in Scilab. Introduction to Scilab is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. After reading this book, you will come away with sample code that can be re-purposed and applied to your own projects using Scilab. What You'll Learn Apply sample code to your engineering or science problems Work with Scilab arrays, functions, and loops Use Scilab’s plotting functions for data visualization Solve numerical computing and computational engineering problems with Scilab Who This Book Is For Engineers, scientists, researchers, and students who are new to Scilab. Some prior programming experience would be helpful but not required.

With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians.

Scilab and its Scicos block diagram graphical editor, with a special emphasis on modeling and simulation tools. The first part is a detailed Scilab tutorial, and the second is dedicated to modeling and simulation of dynamical systems in Scicos. The concepts are illustrated through numerous examples, and all code used in the book is available to the reader.