This book introduces probabilistic modelling and explores its role in solving a broad spectrum of engineering problems that arise in Information Technology (IT).
This book presents a comprehensive series of methods in nonsmooth optimization, with a particular focus on their application in stochastic programming and dedicated algorithms for decision-making under uncertainty.
This edited volume on machine learning and big data analytics (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, International Association of Academicians (IAASSE), and Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and Data Science.
This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field.
This book offers a novel multi-portfolio approach and stochastic programming formulations for modeling and solving contemporary supply chain risk management problems.
This book explores the independence of official statistics and describes the various legal and professional norms, institutional arrangements, instruments and practices that statisticians have developed over recent decades to protect their work from political interference.
This textbook provides a comprehensive exploration of anomalous stochastic processes and extreme events, commonly referred to as "e;black swans,"e; with a particular focus on (multi-)fractal approaches and continuous-time random walks.
Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability.
Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches.
A recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of LMI-based convex optimization methods.
Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error.
"e;Optimal Observation for Cyber-physical Systems"e; addresses the challenge, fundamental to the design of wireless sensor networks (WSNs), presented by the obligatory trade-off between precise estimates and system constraints.
Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others).
"e;Failure Rate Modeling for Reliability and Risk"e; focuses on reliability theory, and to the failure rate (hazard rate, force of mortality) modeling and its generalizations to systems operating in a random environment and to repairable systems.
Aimed primarily at graduate students and researchers, this text is a comprehensive course in modern probability theory and its measure-theoretical foundations.
Stochastic control is one of the methods being used to find optimal decision-making strategies in fields such as operations research and mathematical finance.
Identification of Continuous-time Models from Sampled Data presents an up-to-date view of this active area of research, describing recent methods and software tools and offering new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay.
In order to build a successful, Java-based application it is important to have a clear understanding of the principles underlying the various financial models.
Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices.
The applications of image-based measurement are many and various: image-guided surgery, mobile-robot navigation, component alignment and photogrammetry, among others.
Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results.
A problem of broad interest - the estimation of the spectral gap for matrices or differential operators (Markov chains or diffusions) - is covered in this book.
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences.
Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach.
Safety critical and high-integrity systems, such as industrial plants and economic systems can be subject to abrupt changes - for instance due to component or interconnection failure, and sudden environment changes etc.
This book aims to present a state-of-the-art survey of theories and methods of reliability, maintenance, and warranty with emphasis on multi-unit systems, and to reflect current hot topics: imperfect maintenance, economic dependence, opportunistic maintenance, quasi-renewal processes, warranty with maintenance and economic dependency, and software testing and maintenance.
Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work.
Stochastic differential equations play an increasingly important role in modeling the dynamics of a large variety of systems in the natural sciences, and in technological applications.
Foresight in an engineering enterprise can make the difference between success and failure, and can be vital to the effective control of industrial systems.
It is said that six sigma methods are vital to survive, let alone thrive, in today's competitive markets, but what are these methods and how or when do we use them?
