Probability and statistics

The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data.

This book gives an introduction to the theoretical and computational fluid dynamics of a compressible fluid.

Stochastic geometry deals with models for random geometric structures.

This book offers a new look at well-established quantification theory for categorical data, referred to by such names as correspondence analysis, dual scaling, optimal scaling, and homogeneity analysis.

Other volumes in the Wiley Series in Probability and MathematicalStatistics, Ralph A.

It is well-known that modern stochastic calculus has been exhaustively developed under usual conditions.

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease.

Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners.

This book is concerned with a class of discrete-time stochastic control processes known as controlled Markov processes (CMP's), also known as Markov decision processes or Markov dynamic programs.

Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials gives a thorough presentation of state-of-the-art methods for early phase clinical trials.

This book focuses on the application and development of information geometric methods in the analysis, classification and retrieval of images and signals.

What Makes Variables Random: Probability for the Applied Researcher provides an introduction to the foundations of probability that underlie the statistical analyses used in applied research.

With the critical role of statistics in the design, conduct, analysis and reporting of clinical trials or observational studies intended for regulatory purposes, numerous guidelines have been issued by regulatory authorities around the world focusing on statistical issues related to drug development.

This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research.

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019.

In this book, we talk about many of the tools and techniques of qual- ity.

Applications of Nonlinear Programming to Optimization and Control is a collection of papers presented at the Fourth International Federation of Automatic Control Workshop by the same title, held in San Francisco, California on June 20-21, 1983.

This book presents recent advances (from 2008 to 2012) concerning use of the Naive Bayes model in unsupervised word sense disambiguation (WSD).

The Prague Symposia on Asymptotic Statistics represent a twenty years' tradi- tion of contacts between Czech mathematical statisticians and the conference partic- ipants.

The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now.

Probabilistic models have much to offer to philosophy.

Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them.

This book represents the porceedings of the 1989 Safety and Reliability Society Symposium held in Bath on the 11th and 12th of October on that topic.

Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting.

Mobile crowdsensing (MCS) is emerging as a novel sensing paradigm in the Internet of Things (IoTs) due to the proliferation of smart devices (e.

Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines.

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model.

Non-linear stochastic systems are at the center of many engineering disciplines and progress in theoretical research had led to a better understanding of non-linear phenomena.

This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out.

Praise for the Second Edition This book should be an essential part of the personal library of every practicing statistician.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project.

To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels.

The definitive one-stop resource on structural equation modeling (SEM) from leading methodologists is now in a significantly revised second edition.

This engaging undergraduate textbook uses research relevant to students'' lives to teach research methodology.

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions.

This book constitutes the thoroughly refereed post-proceedings of the 12th International Workshop on Coalgebraic Methods in Computer Science, CMCS 2014, colocated with ETAPS 2014, held in Grenoble, France, in April 2014.

Elections are random events.

Now in its third edition and supplemented with more online material, this book aims to make the "e;new"e; information-based (rather than gene-based) bioinformatics intelligible both to the "e;bio"e; people and the "e;info"e; people.

This book constitutes the conference proceedings of the 10th International Conference on Network Games, Control and Optimization, NETGCOOP 2020, held in Cargese, Corsica, France, in September 2021*.

This contributed volume explores the emerging intersection between big data analytics and genomics.

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature.

This book presents selected peer-reviewed contributions from the International Conference on Time Series and Forecasting, ITISE 2018, held in Granada, Spain, on September 19-21, 2018.

The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance - MAF2024.

Digital Image Processing with C++: Implementing Reference Algorithms with the CImg Library presents the theory of digital image processing and implementations of algorithms using a dedicated library.

John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award.

Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009).

The Dirichlet distribution appears in many areas of application, which include modelling of compositional data, Bayesian analysis, statistical genetics, and nonparametric inference.