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 textbook combines the history of synthetic geometry, centered on the years 1800-1855, with a theorem-proof exposition of the geometry developed in those years.
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 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.
Based on over twenty years' experience as supervisor and external examiner of project work in mathematics, Phil Dyke shows you how to get the best out of degree projects and case studies in mathematics.
Designed to form the basis of an undergraduate course in mathematical finance, this book builds on mathematical models of bond and stock prices and covers three major areas of mathematical finance that all have an enormous impact on the way modern financial markets operate, namely: Black-Scholes' arbitrage pricing of options and other derivative securities; Markowitz portfolio optimization theory and the Capital Asset Pricing Model; and interest rates and their term structure.
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.
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.
Part of my lecturing work in the School of Mathematics at the University of Leeds involved teaching quantum mechanics and statistical mechanics to mathematics undergraduates, and also mathematical methods to undergraduate students in the School of Electronic and Electrical Engineering at the University.
"e;Robust Control for Uncertain Networked Control Systems with Random Delays"e; addresses the problem of analysis and design of networked control systems when the communication delays are varying in a random fashion.
Falling Liquid Films gives a detailed review of state-of-the-art theoretical, analytical and numerical methodologies, for the analysis of dissipative wave dynamics and pattern formation on the surface of a film falling down a planar inclined substrate.
"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.
As we enter the third decade of the World Wide Web (WWW), the textual revolution has seen a tremendous change in the availability of online information.
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.
Interval computing combined with fuzzy logic has become an emerging tool in studying artificial intelligence and knowledge processing (AIKP) applications since it models uncertainties frequently raised in the field.
"e;Computational Surface and Roundness Metrology"e; provides an extraordinarily practical and hands-on approach towards understanding the diverse array of mathematical methods used in surface texture and roundness analysis.
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.
Based on over 15 years' experience in the design and delivery of successful first-year courses, this book equips undergraduates with the mathematical skills required for degree courses in economics, finance, management and business studies.
Mathematical Linguistics introduces the mathematical foundations of linguistics to computer scientists, engineers, and mathematicians interested in natural language processing.
