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.
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.
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 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 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 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 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.
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.
Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed.
This book provides an introduction to the mathematical modelling of real world financial markets and the rational pricing of derivatives, which is part of the theory that not only underpins modern financial practice but is a thriving area of mathematical research.
Iterative Methods for Queuing and Manufacturing Systems introduces the recent advances and developments in iterative methods for solving Markovian queuing and manufacturing problems.
Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: How does a machine learn a concept on the basis of examples?
Written by Nick Bingham, Chairman and Professor of Statistics at Birkbeck College, and Rudiger Kiesel, an "e;up-and-coming"e; academic, Risk Neutrality will benefit the Springer Finance Series in many ways.
Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs.
Recent Advances in System Reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference.
Randomized Algorithms discusses two problems of fine pedigree: counting and generation, both of which are of fundamental importance to discrete mathematics and probability.
Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student.
Over the past decades, although stochastic system control has been studied intensively within the field of control engineering, all the modelling and control strategies developed so far have concentrated on the performance of one or two output properties of the system.
Developed from a set of lecture notes by Professor Kamen and since developed and refined by both authors, this introductory yet comprehensive study is a prime example in its field.
