Mathematical and statistical software

Many books teach computational statistics.

Intuitive Probability and Random Processes using MATLAB(R) is an introduction to probability and random processes that merges theory with practice.

Statistical Modeling and Analysis for Complex Data Problems treats some of today's more complex problems and it reflects some of the important research directions in the field.

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness.

"e;Over 170 figures are included in the book.

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics.

S-PLUS is a powerful tool for interactive data analysis, the creation of graphs, and the implementation of customized routines.

R is an Open Source implementation of the well-known S language.

S is a high-level language for manipulating, analysing and displayingdata.

While SAS and SPSS have many things in common, R is very different.

In recent years developments in statistics have to a great extent gone hand in hand with developments in computing.

Monte Carlo simulation has become one of the most important tools in all fields of science.

This book provides an accelerated introduction to Maple for scientific programmers who already have experience in other computer languages (such as C, Pascal, or FORTRAN).

S is a powerful environment for the statistical and graphical analysis of data.

Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience.

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.

Where do I start?

System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today's debugging team's point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources.

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.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts.

A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.

Tough Test Questions?

Master the Statistical Techniques for Six Sigma Operations, While Boosting Your Excel and Minitab Skills!

Need to Learn MATLAB?

Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are.

MatLab, Third Edition is the only book that gives a full introduction to programming in MATLAB combined with an explanation of the software's powerful functions, enabling engineers to fully exploit its extensive capabilities in solving engineering problems.

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

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level.

A self-contained introduction to abstract interpretation–based static analysis, an essential resource for students, developers, and users.

An introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.

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.

Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms.

Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting.

This book presents a rigorous mathematical development of soil water and contaminant flow in variably saturated and saturated soils.

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced.

Experiments, surveys, measurements, and observations all generate data.

This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility.

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students.

The application and interpretation of statistics are central to ecological study and practice.

The application and interpretation of statistics are central to ecological study and practice.

Computational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology.

Complex systems that bridge the traditional disciplines of physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computers.

New Statistical tools are changing the wau in which scientists analyze and interpret data and models.

R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences.

R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences.

Statistical methods are a key tool for all scientists working with data, but learning the basic mathematical skills can be one of the most challenging components of a biologist's training.

Statistical methods are a key tool for all scientists working with data, but learning the basic mathematical skills can be one of the most challenging components of a biologist's training.