Mathematical and statistical software

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.

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.

Hash tables can do a lot more than you might think!

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 textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata.

Learn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote.

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.

Exceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios.

Quickly and Easily Write Dynamic DocumentsSuitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting.

Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the various parties involved.

The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences.

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis.

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments.

If you know how to program, you're ready to tackle Bayesian statistics.

R, a free and open source program, is one of the most powerful and the fastest-growing statistics program.

This practical, example-driven introduction teaches the foundations of the Mathematica language so it can be applied to solving concrete problems.

This book provides the essential theoretical tools for stochastic modeling.

Praise for RiskGrade Your Investments "e;In the same way that the introduction of RiskMetrics raised the level of the discussion (and sometimes debate) regarding market risk measurement and management at large financial institutions, the introduction of RiskGrades and this book represent a major step in the understanding and application of risk measurement and management techniques by individual investors.

This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time.

Sampling from the posterior distribution and computing posterior quanti- ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation.

Explore essential quantum computing algorithms and master concepts intuitively with minimal math expertise requiredKey FeaturesLearn the fundamentals with an introduction to matrix arithmeticWrite quantum computing programs in Qiskit-IBM's publicly available quantum computing websiteEmail your questions directly to the author-no question is too elementaryPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigate the quantum computing spectrum with this book, bridging the gap between abstract, math-heavy texts and math-avoidant beginner guides.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition.

Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians.

The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work.

Learn window function foundational concepts through a cookbook-style approach, beginning with an introduction to the OVER() clause, its various configurations in terms of how partitions and window frames are created, and how data is sorted in the partition so that the window function can operate on the partition data sets.

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

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

Generate reports with style!

This text covers topics in algebraic geometry and commutative algebra with a strong perspective toward practical and computational aspects.

This book explains the key features of Maple, with a focus on showing how things work, and how to avoid common problems.

* Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data.

This book is written for students at bachelor and master programs and has four different purposes, which split the book into four parts: 1.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view.

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

This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area.

This book covers the basics of numerical methods.

This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology.

Problem Solving and Data Analysis Using Minitab A clear and easy guide to six Sigma methodology Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving.

Methods of risk analysis and the outcome of particular evaluations and predictions are covered in detail in this proceedings volume, whose contributions are based on invited presentations from Professor Mei-Ling Ting Lee's 2011 symposium on Risk Analysis and the Evaluation of Predictions.

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

MATLAB is a high-level language and environment for numerical computation, visualization, and programming.

Statistics with JMP: Hypothesis Tests, ANOVA and Regression Peter Goos, University of Leuven and University of Antwerp, Belgium David Meintrup, University of Applied Sciences Ingolstadt, Germany A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression.