Mathematical and statistical software

This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more.

Some probability problems are so difficult that they stump the smartest mathematicians.

Computational physics is a rapidly growing subfield of computational science, in large part because computers can solve previously intractable problems or simulate natural processes that do not have analytic solutions.

Key FeaturesBook DescriptionJulia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second.

Build and backtest your algorithmic trading strategies to gain a true advantage in the marketKey FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook DescriptionCreating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money.

Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to utilize Python's libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionPython, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way.

Key FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsWhat you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you.

For more than 30 years, this comprehensive manual has been the standard introduction and complete reference for writing articles and books containing mathematical formulas.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more.

This book is of interest to mathematicians and computer scientists working in finite mathematics and combinatorics.

This book presents fundamentals in MATLAB programming, including data and statement structures, control structures, function writing and bugging in MATLAB programming, followed by the presentations of algebraic computation, transcendental function evaluations and data processing.

This book deals with the visualization and exploration of invariant sets (fractals, strange attractors, resonance structures, patterns etc.

The advent of mathematical software has been one of the most important events in mathematics.

Eschewing a more theoretical approach, Portfolio Optimization shows how the mathematical tools of linear algebra and optimization can quickly and clearly formulate important ideas on the subject.

The personal computer has made statistical analysis easier and cheaper.

The principal aim of this book is to introduce university level mathematics - both algebra and calculus.

This unique book provides a streamlined, self-contained and modern text for a one-semester mathematical methods course with an emphasis on concepts important from the application point of view.

This is the first book of its kind which teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R.

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures.

This book presents a study of the COVID-19 pandemic using computational social scientific analysis that draws from, and employs, statistics and simulations.

The book covers computational statistics, its methodologies and applications for IoT device.

This book brings together two major trends: data science and blockchains.

This book provides an accessible introduction and practical guidelines to apply asymmetric multidimensional scaling, cluster analysis, and related methods to asymmetric one-mode two-way and three-way asymmetric data.

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields.

This book discusses all major topics on survey sampling and estimation.

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures.

This book presents theoretical modeling and numerical simulations applied to drive several applications towards Industrial Revolution 4.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved.

This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms.

This book features selected papers presented at the 2nd International Conference on Advanced Computing Technologies and Applications, held at SVKM's Dwarkadas J.

This book discusses enterprise hierarchies, which view a target system with varying degrees of abstraction.

This book is a product of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) to be held in Langkawi in November 2017.

 Deep Learning with R introduces deep learning and neural networks using the R programming language.

This book discusses recent developments in mathematical programming and game theory, and the application of several mathematical models to problems in finance, games, economics and graph theory.

This is an introductory text on a broad class of statistical estimators that are minimizers of convex functions.

This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research.

This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software.

The purpose of this book is to thoroughly prepare the reader for applied research in clustering.

Recent advances in the understanding of star formation and evolution have been impressive and aspects of that knowledge are explored in this volume.

This book provides practical applications of doubly classified models by using R syntax to generate the models.

This book is a valuable read for a diverse group of researchers and practitioners who analyze assessment data and construct test instruments.

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms.

This book discusses examples in parametric inference with R.

El presente libro introduce distintas áreas de matemática dirigidas a estudiantes de carreras vinculadas con la ingeniería en general, la informática o las ciencias de la computación.

The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance.

This book promotes the experimental mathematics approach in the context of secondary mathematics curriculum by exploring mathematical models depending on parameters that were typically considered advanced in the pre-digital education era.

The theory of U-statistics goes back to the fundamental work of Hoeffding [1], in which he proved the central limit theorem.

With strong numerical and computational focus, this book serves as an essential resource on the methods for functional neuroimaging analysis, diffusion weighted image analysis, and longitudinal VBM analysis.

This volume provides essential guidance for transforming mathematics learning in schools through the use of innovative technology, pedagogy, and curriculum.