stochastic simulation algorithms and analysis 57 stochastic modelling and applied probability

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Stochastic Simulation Algorithms And Analysis

Author : Søren Asmussen
ISBN : 9780387690339
Genre : Mathematics
File Size : 40. 78 MB
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Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides 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; the second half discusses model-specific algorithms. Exercises and illustrations are included.

Stochastic Simulation And Monte Carlo Methods

Author : Carl Graham
ISBN : 9783642393631
Genre : Mathematics
File Size : 83. 45 MB
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In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners’ aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems. The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of Itô integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view. The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations.

Stochastic Simulation

Author : Brian D. Ripley
ISBN : 9780470317389
Genre : Mathematics
File Size : 66. 23 MB
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WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .this is a very competently written and useful addition to the statistical literature; a book every statistician should look at and that many should study!" —Short Book Reviews, International Statistical Institute ". . .reading this book was an enjoyable learning experience. The suggestions and recommendations on the methods [make] this book an excellent reference for anyone interested in simulation. With its compact structure and good coverage of material, it [is] an excellent textbook for a simulation course." —Technometrics ". . .this work is an excellent comprehensive guide to simulation methods, written by a very competent author. It is especially recommended for those users of simulation methods who want more than a 'cook book'. " —Mathematics Abstracts This book is a comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. It covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation. Supported by the relevant mathematical theory, the text contains a great deal of unpublished research material, including coverage of the analysis of shift-register generators, sensitivity analysis of normal variate generators, analysis of simulation output, and more.

Markov Chains And Stochastic Stability

Author : Sean Meyn
ISBN : 9780521731829
Genre : Mathematics
File Size : 76. 64 MB
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New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996.

An Introduction To Computational Stochastic Pdes

Author : Gabriel J. Lord
ISBN : 9781139915779
Genre : Mathematics
File Size : 82. 99 MB
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This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method. Later chapters apply the theory of random fields to the numerical solution of elliptic PDEs with correlated random data, discuss the Monte Carlo method, and introduce stochastic Galerkin finite-element methods. Finally, stochastic parabolic PDEs are developed. Assuming little previous exposure to probability and statistics, theory is developed in tandem with state-of-the-art computational methods through worked examples, exercises, theorems and proofs. The set of MATLAB codes included (and downloadable) allows readers to perform computations themselves and solve the test problems discussed. Practical examples are drawn from finance, mathematical biology, neuroscience, fluid flow modelling and materials science.


Author : Hansjöerg Albrecher
ISBN : 9781119419945
Genre : Mathematics
File Size : 29. 14 MB
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Reinsurance: Actuarial and Statistical Aspects provides a survey of both the academic literature in the field as well as challenges appearing in reinsurance practice and puts the two in perspective. The book is written for researchers with an interest in reinsurance problems, for graduate students with a basic knowledge of probability and statistics as well as for reinsurance practitioners. The focus of the book is on modelling together with the statistical challenges that go along with it. The discussed statistical approaches are illustrated alongside six case studies of insurance loss data sets, ranging from MTPL over fire to storm and flood loss data. Some of the presented material also contains new results that have not yet been published in the research literature. An extensive bibliography provides readers with links for further study.

Stochastic Modeling

Author : Barry L. Nelson
ISBN : 9780486139944
Genre : Mathematics
File Size : 77. 52 MB
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Coherent introduction to techniques also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Includes formulation of models, analysis, and interpretation of results. 1995 edition.

Simulating Copulas

Author : Jan-Frederik Mai
ISBN : 9781908977588
Genre : Mathematics
File Size : 42. 55 MB
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This book provides the reader with a background on simulating copulas and multivariate distributions in general. It unifies the scattered literature on the simulation of various families of copulas (elliptical, Archimedean, Marshall-Olkin type, etc.) as well as on different construction principles (factor models, pair-copula construction, etc.). The book is self-contained and unified in presentation and can be used as a textbook for advanced undergraduate or graduate students with a firm background in stochastics. Alongside the theoretical foundation, ready-to-implement algorithms and many examples make this book a valuable tool for anyone who is applying the methodology. Errata(s) Errata (128 KB) Sample Chapter(s) Chapter 1: Introduction (1,016 KB) Chapter 4: Elliptical Copulas (857 KB) Contents:IntroductionArchimedean CopulasMarshall–Olkin CopulasElliptical CopulasPair Copula ConstructionsSampling Univariate Random VariablesThe Monte Carlo Method Readership: Advanced undergraduate and graduate students in probability calculus and stochastics, practitioners who implement models in the financial industry and scientists. Keywords:Copula;Simulation;Monte Carlo;Random Vector;Dependence ModelKey Features:Explicit focus on stochastic representations of copulas in contrast to an analytical perspectiveEasy-to-implement simulation schemes given as pseudo codeExplicit focus on high-dimensional modelsFocus on applicability of models, e.g. to portfolio credit risk or insuranceReviews:“The book is essentially self-contained, as the reader interested in copulas from the simulation point of view will find all necessary material in it, including an introduction to copulas if he has never been exposed to them. Both the theoretical and practical frameworks emerge quite clearly from the book. In any case, the rich bibliography contains all the references required for further in-depth analyses of specific issues. I think that the authors did a very good job, filling a gap in the statistical literature and providing a contribution that is going to be particularly helpful to statisticians without a specific background in copulas. ”Mathematical Reviews

Martingale Methods In Financial Modelling

Author : Marek Musiela
ISBN : 9783662221327
Genre : Mathematics
File Size : 57. 98 MB
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A comprehensive and self-contained treatment of the theory and practice of option pricing. The role of martingale methods in financial modeling is exposed. The emphasis is on using arbitrage-free models already accepted by the market as well as on building the new ones. Standard calls and puts together with numerous examples of exotic options such as barriers and quantos, for example on stocks, indices, currencies and interest rates are analysed. The importance of choosing a convenient numeraire in price calculations is explained. Mathematical and financial language is used so as to bring mathematicians closer to practical problems of finance and presenting to the industry useful maths tools.

Monte Carlo Methods In Financial Engineering

Author : Paul Glasserman
ISBN : 9780387216171
Genre : Mathematics
File Size : 20. 27 MB
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From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not." --Glyn Holton, Contingency Analysis

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