Monte-Carlo Methods & Stochastic Pr, Gobet

Автор: Shreve
Название: Stochastic Calculus for Finance I
ISBN: 0387401008 ISBN-13(EAN): 9780387401003
Издательство: Springer
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Цена: 8384.00 р.
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Описание: Developed for the professional Master`s program in Computational Finance at Carnegie Mellon, the leading financial engineering program in the U.S. Has been tested in the classroom and revised over a period of several yearsExercises conclude every chapter;

Автор: Glasserman
Название: Monte Carlo Methods in Financial Engineering
ISBN: 0387004513 ISBN-13(EAN): 9780387004518
Издательство: Springer
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Цена: 11179.00 р.
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Описание: 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."

Автор: Fuente, Angel de la.
Название: Mathematical methods and models for economists
ISBN: 0521585295 ISBN-13(EAN): 9780521585293
Издательство: Cambridge Academ
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Цена: 8554.00 р.
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Описание: This book is intended as a textbook for a first-year PhD course in mathematics for economists and as a reference for graduate students in economics. It provides a self-contained, rigorous treatment of most of the concepts and techniques required to follow the standard first-year theory sequence in micro and macroeconomics.

Автор: Gardiner, Crispin W.
Название: Stochastic methods
ISBN: 3540707123 ISBN-13(EAN): 9783540707127
Издательство: Springer
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Цена: 11179.00 р.
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Описание: In the third edition of this classic the chapter on quantum Marcov processes has been replaced by a chapter on numerical treatment of stochastic differential equations to make the book even more valuable for practitioners.

Название: Stochastic Simulation and Monte Carlo Methods
ISBN: 3642393624 ISBN-13(EAN): 9783642393624
Издательство: Springer
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Цена: 8384.00 р.
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Описание: 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.

Автор: Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G.
Название: Monte Carlo Methods in Bayesian Computation
ISBN: 0387989358 ISBN-13(EAN): 9780387989358
Издательство: Springer
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Цена: 20962.00 р.
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Описание: This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches.The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.