Probability & Statistics

Deep learning is an important element of artificial intelligence, especially in applications such as image classification in which various architec...

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack t...

Discrete phenomena are an important aspect of various complex systems, acting both as underlying driving mechanisms and as manifestations of divers...

Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, t...

Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonpa...

Introduction to Probability with Texas Hold'em Examples illustrates both standard and advanced probability topics using the popular poker game of T...

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and App...

A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With Refe...

Learn how to read, understand, and write better SAS programs Understand the key differences between similar SAS syntax and programming approaches S...

Reliability is an essential concept in mathematics, computing, research, and all disciplines of engineering, and reliability as a characteristic is...

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from t...

Originally published in 1923 Chance and Error examines the vagaries of chance, and how this is the result of the interference of yes and no. The bo...

Statistical Methods: An Introduction to Basic Statistical Concepts and Analysis, Second Edition is a textbook designed for students with no prior t...

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of...

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on pa...

This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks. The boo...

Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforwar...

A textbook for a graduate course in the theory of distributions and related topics, for students of applied mathematics or theoretical physics. Int...

This volume serves as a comprehensive introduction to Time Series Analysis (TSA), used commonly in financial and engineering sciences, to demonstra...

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pa...

Based on the authors lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage of statistical infer...

Subjective Well-Being and Social Media shows how, by exploiting the unprecedented amount of information provided by the social networking sites, it...

This book provides a guide to the elements of simulation in statistics, which will include developments and which may be used either as a teaching ...

Principles of Scientific Methods focuses on the fundamental principles behind scientific methods. The book refers to science in a broad sense, incl...