Stochastics

This book is the first comprehensive presentation of a central topic of stochastic geometry: random mosaics that are generated by Poisson processes of hyperplanes.

This book offers accessible probabilistic modelling of relevant financial problems.

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 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.

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.

Lean production, has long been regarded as critical to business success in many industries.

"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.

This volume is composed of invited papers on learning and control.

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation.

Most engineering systems su?

Fractional Brownian motion (fBm) has been widely used to model a number of phenomena in diverse fields from biology to finance.

Mathematical finance has grown into a huge area of research which requires a large number of sophisticated mathematical tools.

Often, real-world problems modeled by Markov decision processes (MDPs) are difficult to solve in practise because of the curse of dimensionality.

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.

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.

Stochastic geometry is a relatively new branch of mathematics.

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.

This book develops robust design and assessment of product and production from viewpoint of system theory, which is quantized with the introduction of brand new concept of preferable probability and its assessment.

This fascinating book begins with fundamental definitions and notations of urn models before moving on to stochastic processes and applications of urn models in the field of finance.

This book is interdisciplinary and unites several areas of applied probability, statistics, and computational mathematics including computer experiments, optimal experimental design, and global optimization.

Monte Carlo Methods are among the most used, and useful, computational tools available today.

This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails.

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 text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis.

This textbook offers a self-contained introduction to probability, covering all topics required for further study in stochastic processes and stochastic analysis, as well as some advanced topics at the interface between probability and functional analysis.

This book is devoted to real analysis methods for the problem of constructing Markov processes with boundary conditions in probability theory.

This 2nd edition of the book focuses on the properties of stationary states in chaotic systems of particles or fluids, setting aside the theory of how these states are achieved.

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.

This self-contained, comprehensive book tackles the principal problems and advanced questions of probability theory and random processes in 22 chapters, presented in a logical order but also suitable for dipping into.

Potential Theory presents a clear path from calculus to classical potential theory and beyond, with the aim of moving the reader into the area of mathematical research as quickly as possible.

The fascinating correspondence between Paul Levy and Maurice Frechet spans an extremely active period in French mathematics during the twentieth century.

This second edition of the popular textbook contains a comprehensive course in modern probability theory, covering a wide variety of topics which are not usually found in introductory textbooks, including: * limit theorems for sums of random variables* martingales* percolation* Markov chains and electrical networks* construction of stochastic processes* Poisson point process and infinite divisibility* large deviation principles and statistical physics* Brownian motion* stochastic integral and stochastic differential equations.

The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation.

Backward stochastic differential equations with jumps can be used to solve problems in both finance and insurance.

The featured review of the AMS describes the author's earlier work in the field of approach spaces as, 'A landmark in the history of general topology'.

Stochastic instantaneous volatility models such as Heston, SABR or SV-LMM have mostly been developed to control the shape and joint dynamics of the implied volatility surface.

Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems begins with an introduction and extensive literature survey.

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.

Risk has been described in the past by a simple measure, such as the variance, and risk attitude is often considered simply a degree of risk aversion.