This volume provides an overview of two of the most important examples of interacting particle systems, the contact process, and the voter model, as well as their many variants introduced in the past 50 years.
Starting with the Shannon-Wiener approach to mathematical information theory, allowing a mathematical "e;measurement"e; of an amount of information, the book begins by defining the terms message and information and axiomatically assigning an amount of information to a probability.
Containing many results that are new, or which exist only in recent research articles, this thoroughly revised third edition of Interest Rate Modeling: Theory and Practice, Third Edition portrays the theory of interest rate modeling as a three-dimensional object of finance, mathematics, and computation.
Analysis of queues is used in a variety of domains including call centers, web servers, internet routers, manufacturing and production, telecommunications, transportation, hospitals and clinics, restaurants, and theme parks.
Containing many results that are new, or which exist only in recent research articles, this thoroughly revised third edition of Interest Rate Modeling: Theory and Practice, Third Edition portrays the theory of interest rate modeling as a three-dimensional object of finance, mathematics, and computation.
The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc.
This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting.
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis.
This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation.
Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data.
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis.
This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation.
A discussion of the basic reliability concepts and models, this book is suitable for students of reliability engineering as well as for those who wish a supplement on applied survival data analysis.
Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change.
Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change.
This book provides a systematic presentation of the major results in the field of the theory of k-out-of-n systems obtained in recent years and their applications for the reliability assessment of high-altitude unmanned platforms.
This book provides a systematic presentation of the major results in the field of the theory of k-out-of-n systems obtained in recent years and their applications for the reliability assessment of high-altitude unmanned platforms.
This textbook offers an approachable introduction to measure-theoretic probability, illustrating core concepts with examples from statistics and engineering.
This textbook offers an approachable introduction to measure-theoretic probability, illustrating core concepts with examples from statistics and engineering.
This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning.
This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning.
This book provides a captivating journey through the realms of classical and quantum systems as it unravels the profound influence that noise may have on their static and dynamic properties.
This book provides a captivating journey through the realms of classical and quantum systems as it unravels the profound influence that noise may have on their static and dynamic properties.
This textbook offers a complete one-semester course in probability, covering the essential topics necessary for further study in the areas of probability and statistics.
This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications.
An Introduction to Applied Statistics offers a comprehensive and accessible foundation in applied statistics, empowering students with the essential concepts and practical skills necessary for data-driven decision-making in today's world.
