Stochastic Programing

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysi

Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today it is used to model a variety of complex systems in the fiel

This book records a unique attempt over a ten-year period to use stochastic optimization in the natural language processing domain. Setting the work against the background of the logical rule-based ap

This volume presents an eclectic mix of original research articles in areas covering the analysis of ordered data, stochastic modeling and biostatistics. These areas were featured in a conference held

This book retraces the history of macroeconomics from Keynes's General Theory to the present. Central to it is the contrast between a Keynesian era and a Lucasian - or dynamic stochastic general equil

This Second Edition features a unique overview that melds the concepts of conditional probability and stochastic processes into real-life applications. By combinin

Covering both classical and quantum approaches, this unique and self-contained book presents the most recent developments in the theory of quadratic stochastic operators and their Markov and related p

This book provides elaborating tools to measure financial risk without assuming that time-invariant stochastic processes drive financial phenomena. Discarding time-invariance as a modeling assumption

The book presents a stochastic analysis based on production risk and application of this method in the industrial sector under production risk where energy use is an input factor. Using South Korea as

This introductory textbook is designed for a one-semester course on queueing theory that does not require a course on stochastic processes as a prerequisite. By integrating the necessary background on

This book covers the most important issues from classical and robust control, deterministic and stochastic control, system identification, and adaptive and iterative control strategies. It covers most

For upper-level undergraduate courses in deterministic and stochastic signals and system engineeringAn Integrative Approach to Signals, Systems and InferenceSignals, Systems and Inference is a compreh

The book presents applications of stochastic calculus to derivative security pricing and interest rate modelling. By focusing more on the financial intuition of the applications rather than the mathem

This book is the first to comprehensively explore elasticity imaging and examines recent, important developments in asymptotic imaging, modeling, and analysis of deterministic and stochastic elastic w

This monograph presents computational techniques and numerical analysis to study conservation laws under uncertainty using the stochastic Galerkin formulation. With the continual growth of computer po

The master thesis of Kevin Berk develops a stochastic model for the electricity demand of small and medium-sized companies that is flexible enough so that it can be used for various business sectors.

This book is about the formulations, theoretical investigations, and practical applications of new stochastic models for fundamental concepts and operations of the discipline of risk management. It al

This text fills a niche between a calculus-based probability course and a stochastic processes course typically taken by upper undergraduate students. While Markov processes are touched on in probabil

Recent developments of probabilistic assessment of systems dependability based on stochastic models issued of graph theory, finite state automaton and language theory, applying as well for static as f

This book contains original research papers by leading experts in the fields of probability theory, stochastic analysis, potential theory and mathematical physics. There is also a historical account o

This book investigates the existence of stochastic and deterministic convergence of real output per worker and the sources of output (physical capital per worker, human capital per worker, total facto

The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations,

Stochastic descriptions of a harmonic oscillator can be obtained by adding additive noise, or/and three types of multiplicative noise: random frequency, random damping and random mass. The first three

"The series of recent financial crises have thrown open the world of quantitative finance and financial modeling. The era of stochastic calculus is over and the time of Ito derivation is at an end. To

The aim of this volume is to provide an extensive account of the most recent advances in statistics for discretely observed Levy processes. These days, statistics for stochastic processes is a lively

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. In principle, they are w

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framew

The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic si

Provides a comprehensive coverage of both the deterministic and stochastic models of life contingencies, risk theory, credibility theory, multi-state models, and an introduction to modern mathematical

Queueing models were successfully applied for material handling and production systems.The book helps practitioners to understand the major influences for inventory and lead times. Stochastic effects

This book is devoted to a number of stochastic models that display scale invariance. It primarily focuses on three issues: probabilistic properties, statistical estimation and simulation of the proces

A careful and accessible exposition of functional analytic methods in stochastic analysis is provided in this book. It focuses on the interrelationship between three subjects in analysis: Markov proce

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews th

This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scat

The textbook introduces both scientific programming and stochastic modeling to undergraduate or graduate students of science, engineering, or commerce. Students are assumed to have taken or be taking

This book introduces a wide variety of mathematical methods, such as signal processing, systems identification, and stochastic differential equations, to an undergraduate audience interested in biolo

This book is organized into five parts, each of which is further organized into several chapters. The five parts are: Part One, Measure Theory; Part Two, Probability Theory, Part Three, Stochastic Pr

This volume presents recent developments in the area of Levy-type processes and more general stochastic processes that behave locally like a Levy process. Although written in a survey style, quite a f

Provides a modern mathematical approach to the design of communication networks for graduate students, blending control, optimization, and stochastic network theories. A broad range of performance analysis tools are discussed, including important advanced topics that have been made accessible to students for the first time. Taking a top-down approach to network protocol design, the authors begin with the deterministic model and progress to more sophisticated models. Network algorithms and protocols are tied closely to the theory, illustrating the practical engineering applications of each topic. The background behind the mathematical analyses is given before the formal proofs and is supported by worked examples, enabling students to understand the big picture before going into the detailed theory. End-of-chapter problems cover a range of difficulties, with complex problems broken into several parts, and hints to many problems are provided to guide students. Full solutions are available