Mathematical and statistical software

This book provides a general framework for learning sparse graphical models with conditional independence tests.

This book provides a general framework for learning sparse graphical models with conditional independence tests.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded third edition of Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded third edition of Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics.

The purpose of this book is to thoroughly prepare diverse areas of researchers in quantification theory.

The purpose of this book is to thoroughly prepare diverse areas of researchers in quantification theory.

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data.

This book provides the essential theoretical tools for stochastic modeling.

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource.

A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming.

The volume features high-quality papers based on the presentations at the ICOSAHOM 2020+1 on spectral and high order methods.

The volume features high-quality papers based on the presentations at the ICOSAHOM 2020+1 on spectral and high order methods.

This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021.

Heuristics are strategies using readily accessible, loosely applicable information to control problem solving.

Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data.

This book introduces Probability Theory with R software and explains abstract concepts in a simple and easy-to-understand way by combining theory and computation.

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making.

Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization.

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization.

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

This research monograph utilizes exact and Monte Carlo permutation statistical methods to generate probability values and measures of effect size for a variety of measures of association.

This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations.

This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions.

This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019.

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.

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.

A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework.

This book presents advanced research on smart health technologies, focusing on the innovative transformations in diagnosis and treatment planning using medical imaging and data analysed by data science techniques.

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 studies data, analytics, and intelligence using Boolean structure.

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.

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.