الصفحة 1
الصفحة 1
Vorticity, Statistical Mechanics, and Monte Carlo Simulation
This book is drawn from across many active fields of mathematics and physics, and has connections to atmospheric dynamics, spherical codes, graph theory, constrained optimization problems, Markov Chains, and Monte Carlo methods. It addresses how to access interesting, original, and publishable research in statistical modeling of large-scale flows and several related fields. The authors f this book explicitly reach around the major branches of mathematics and physics, showing how the use of a few straightforward approaches can create a cornucopia of intriguing questions and the tools to answer them. In reading this book, the reader will learn how to research a topic and how to understand statistical mechanics treatments of fluid dynamics.
Uncertainty in Engineering : Introduction to Methods and Applications
Provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling.
The Science of Phototherapy : An Introduction
Phototherapy exemplifies scientific medicine. The major advances have resulted from effective collaborations between basic researchers and clinicians. This book is directed to clinicians and basic researchers who are interested in current and emerging implementations of phototherapy. It can serve as an introductory reference and a textbook for advanced undergraduate and graduate courses in medical physics and biomedical engineering. The emphasis is on the science underlying the various phototherapy procedures, which encompasses aspects of classical and molecular photophysics, biological photochemistry, photobiology and biophotonics. Topics that do not usually appear in other general sources include the theory and applications of tissue optics, Monte Carlo simulation, light dosimetry, and analytical modeling of laser surgery. Many illustrative problems with answers are provided to exemplify the more quantitative aspects of each topic.
The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation
This book covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration including Gibbs sampling, and other MCMC techniques.
Switching and learning in feedback systems : European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10, 2003, Revised Lectures and Selected Papers
A central theme in the study of dynamic systems is the modelling and control of uncertain systems. While ‘uncertainty’ has long been a strong motivating factor behind many techniques developed in the modelling, control, statistics and mathematics communities, the past decade, in particular, has witnessed remarkable progress in this area with the emergence of a number of powerful new methods for both modelling and controlling uncertain dynamic systems. The specific objective of this book is to describe and review some of these exciting new approaches within a single volume. Our approach was to invite some of the leading researchers in this area to contribute to this book by submitting both tutorial papers on their speci?c area of research, and to submit more focussed research papers to document some of the latest results in the area. We feel that collecting some of the main results together in this manner is particularly important as many of the important ideas that emerged in the past decade were derived in a variety of academic disciplines.
Structural, Syntactic, and Statistical Pattern Recognition ; Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006, Proceedings
This book constitutes the refereed proceedings of the 11th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2006, held jointly in Hong Kong, China in August 2006 as a satellite event of the 18th International Conference of Pattern Recognition, ICPR 2006. The 38 revised full papers and 61 revised poster papers presented together with 4 invited papers were carefully reviewed and selected from 217 submissions. The papers are organized in topical sections on image analysis, vision, character recognition, bayesian networks, graph-based methods, similarity and feature extraction, image and video, vision, kernel-based methods, recognition and classification, similarity.
Stochastic Simulation : Algorithms and Analysis
Povides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms.
Stochastic Optimization
The search for optimal solutions pervades our daily lives. From the scientific point of view, optimization procedures play an eminent role whenever exact solutions to a given problem are not at hand or a compromise has to be sought, e.g. to obtain a sufficiently accurate solution within a given amount of time. This book addresses stochastic optimization procedures in a broad manner, giving an overview of the most relevant optimization philosophies in the first part. The second part deals with benchmark problems in depth, by applying in sequence a selection of optimization procedures to them. While having primarily scientists and students from the physical and engineering sciences in mind, this book addresses the larger community of all those wishing to learn about stochastic optimization techniques and how to use them.
Stochastic Numerics for the Boltzmann Equation
Stochastic numerical methods play an important role in large scale computations in the applied sciences. The first goal of this book is to give a mathematical description of classical direct simulation Monte Carlo (DSMC) procedures for rarefied gases, using the theory of Markov processes as a unifying framework. The second goal is a systematic treatment of an extension of DSMC, called stochastic weighted particle method. This method includes several new features, which are introduced for the purpose of variance reduction (rare event simulation). Rigorous convergence results as well as detailed numerical studies are presented.
Stochastic Calculus of Variations in Mathematical Finance
This book starts with an exposition from scratch of this theory. Greeks (price sensitivities) are reinterpreted in terms of Malliavin calculus. Integration by parts formulae provide stable Monte Carlo schemes for numerical valuation of digital options. Finite-dimensional projections of infinite-dimensional Sobolev spaces lead to Monte Carlo computations of conditional expectations useful for computing American options.
Statistical and Computational Inverse Problems
The book develops the statistical approach to inverse problems with an emphasis on modeling and computations. The framework is the Bayesian paradigm, where all variables are modeled as random variables, the randomness reflecting the degree of belief of their values, and the solution of the inverse problem is expressed in terms of probability densities. The book discusses in detail the construction of prior models, the measurement noise modeling and Bayesian estimation. Markov Chain Monte Carlo-methods as well as optimization methods are employed to explore the probability distributions. The book is intended to researchers and advanced students in applied mathematics, computational physics and engineering. The first part of the book can be used as a text book on advanced inverse problems courses.
Soft Computing Applications in Business
Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field.The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques.
Slow Rarefied Flows : Theory and Application to Micro-Electro-Mechanical Systems
Presents the mathematical tools used to deal with problems related to slow rarefied flows, with particular attention to basic concepts and problems which arise in the study of micro- and nanomachines. The mathematical theory of slow flows is presented in a practically complete fashion and provides a rigorous justification for the use of the linearized Boltzmann equation, which avoids costly simulations based on Monte Carlo methods.
Rugged Free Energy Landscapes : Common Computational Approaches to Spin Glasses, Structural Glasses and Biological Macromolecules
This collection of lectures and tutorial reviews by renowned experts focusses on the common computational approaches in use to unravel the static and dynamical behaviour of complex physical systems at the interface of physics, chemistry and biology. Paradigmatic examples of condensed matter physics are spin and structural glasses and protein folding, as well as their aggregation and adsorption to hard and soft surfaces, in physico-chemical biology.
Reliability-based Structural Design
Reliability-based Structural Design provides readers with an understanding of the fundamentals and applications of structural reliability, stochastic finite element method, reliability analysis via stochastic expansion, and optimization under uncertainty. Probability theory, statistic methods, and reliability analysis methods including Monte Carlo sampling, Latin hypercube sampling, first and second-order reliability methods, stochastic finite element method, and stochastic optimization are discussed. In addition, the use of stochastic expansions, including polynomial chaos expansion and Karhunen-Loeve expansion, for the reliability analysis of practical engineering problems is also examined. Detailed examples of practical engineering applications including an uninhabited joined-wing aircraft and a supercavitating torpedo are presented to illustrate the effectiveness of these methods.
Real Options Valuation : The Importance of Interest Rate Modelling in Theory and Practice
Managerial decision-making during the lifetime of a project can have im portant implications on project handling and its contribution to shareholder value. Traditional capital budgeting methods (in particular methods based on net present value) fail to capture the role of managerial degrees of free dom and therefore tend to lead to a systematic undervaluation of the project. In contrast, the real options approach to investment analysis characterizes decision-making flexibility in terms of (real) option rights which can be eval uated analogously to financial options using contingent-claims pricing tech niques widely used in capital markets. The research carried out by Marcus Schulmerich analyzes real options for n- constant and stochastic interest rates versus constant interest rates. Analyzing stochastic interest rates in the context of real options valuation is of particular relevance given their long time to maturity which makes them more vulnera ble to interest rate risk than short-term financial options. To date, there has not been a comprehensive review of this issue in the academic literature. The fact that interest rates have fiuctuated widely over the recent years further highlights the need for studying this issue.
Randomized Algorithms for Analysis and Control of Uncertain Systems
The main objective of this book is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of uncertain systems. Using so-called "randomized algorithms", this emerging area of research guarantees a reduction in the computational complexity of classical robust control algorithms and in the conservativeness of methods like H-infinity control.
Quantum Computation in Solid State Systems
The aim of Quantum Computation in Solid State Systems is to report on recent theoretical and experimental results on the macroscopic quantum coherence of mesoscopic systems, as well as on solid state realization of qubits and quantum gates. Particular attention has been given to coherence effects in Josephson devices. Other solid state systems, including quantum dots, optical, ion, and spin devices which exhibit macroscopic quantum coherence are also discussed. Quantum Computation in Solid State Systems discusses experimental implementation of quantum computing and information processing devices, and in particular observations of quantum behavior in several solid state systems.
Probabilità statistica e simulazione : Programmi applicativi scritti con scilab = Statistical probability and simulation : Application programs written with Scilab
Il volume contiene in forma compatta il programma svolto negli insegnamenti introduttivi di statistica e tratta alcuni argomenti indispensabili per l'attività di ricerca, come ad esempio i metodi di simulazione Monte Carlo, le procedure di minimizzazione e le tecniche di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Numerosi esempi ed esercizi risolti valorizzano l'opera ed aiutano il lettore nella comprensione dei punti più difficili ed importanti. Come ulteriore supporto, questa seconda edizione contiene molti programmi applicativi scritti col software libero Scilab, scaricabili dal sito web creato dagli autori.
Premiers pas en simulation = First steps in simulation
Why simulation techniques? Simulation methods, designed for use in statistics and operations research, have experienced and continue to develop rapidly due to the extraordinary evolution of computers. Applications are found in industry and in economics, or even social sciences, in particle physics, in astronomy and in many other fields. In many situations, whether in everyday life or in scientific research, the researcher is faced with problems which he seeks solutions on the basis of certain initial assumptions and constraints. To solve this type of problem, there exist analytical methods applicable to situations where the model makes it possible to treat the di? Erent variables by mathematically manageable equations, and numerical methods where the complexity of the model imposes a fragmentation of the problem, in particular by the identification of the various variables which come into play and the study of their interactions. This last approach is often accompanied by a large mass of calculations. Simulation techniques are numerical techniques: to simulate a phenomenon essentially means to carefully reconstruct its evolution.
