Mathematical and statistical software

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

The Encyclopaedia of Discrete Mathematics presents a through, Comprehensive Treatment of the subject using a Flexible Modular format.

This book introduces recently developed mixed finite element methods for large-scale geophysical flows that preserve essential numerical properties for accurate simulations.

This book is a self-guided tour of MATLAB for engineers and life scientists.

This book presents a detailed description of the development of statistical theory.

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

Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets.

This book, first published in 2003, provides a concise but sound treatment of ODEs, including IVPs, BVPs, and DDEs.

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

This volume features selected contributions on a variety of topics related to linear statistical inference.

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

R is open source statistical computing software.

When it comes to business intelligence and analytical capabilities, SAS Visual Analytics is the premier solution for data discovery, visualization, and reporting.

Written by an experienced operations research practitioner with a strong applied mathematics background, this book offers practical insights into how to approach optimization problems, how to develop intelligent and efficient mathematical models and algorithms, and how to implement and deliver software products to customers.

"e;This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques.

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.

Solve your pharmaceutical product development and manufacturing problems using JMP .

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

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.

From the reviews: "e;.

Point and click your way to performing statistics!

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.

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R.

This book presents the basic procedures for using SAS Enterprise Guide to analyse statistical data.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research.

The book is intended to serve as an introductory course in group theory geared towards second-year university students.

This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA).

This book covers Mathematica® for beginners.

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

PsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics.

The authors, Dominique Ladiray and Benoit Quenneville, provide a unique and comprehensive r~view of the X-11 Method of seasonal adjustment.

This book provides a unified view on a new methodology for Machine Translation (MT).

This book provides a unique tour of university mathematics with the help of Python.

Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology.

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

SAS(R) OnDemand for Academics is now the primary software choice for learners.

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

This book presents the state of the art on numerical semigroups and related subjects, offering different perspectives on research in the field and including results and examples that are very difficult to find in a structured exposition elsewhere.

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks.

Computing with Mathematica, Second Edition is engaging and interactive.

If youre considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment.

Physically Unclonable Functions (PUFs) translate unavoidable variations in certain parameters of materials, waves, or devices into random and unique signals.

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data.

This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems.