Foundations of Quantization for Probability Distributions
Foundations of Quantization for Probability Distributions

Author: Siegfried Graf

Publisher: Springer

Published: 2007-05-06

Total Pages: 238

ISBN-13: 3540455779

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Due to the rapidly increasing need for methods of data compression, quantization has become a flourishing field in signal and image processing and information theory. The same techniques are also used in statistics (cluster analysis), pattern recognition, and operations research (optimal location of service centers). The book gives the first mathematically rigorous account of the fundamental theory underlying these applications. The emphasis is on the asymptotics of quantization errors for absolutely continuous and special classes of singular probabilities (surface measures, self-similar measures) presenting some new results for the first time. Written for researchers and graduate students in probability theory the monograph is of potential interest to all people working in the disciplines mentioned above.

Foundations of Computational Mathematics, Budapest 2011
Foundations of Computational Mathematics, Budapest 2011

Author: Society for the Foundation of Computational Mathematics

Publisher: Cambridge University Press

Published: 2013

Total Pages: 249

ISBN-13: 1107604079

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A diverse collection of articles by leading experts in computational mathematics, written to appeal to established researchers and non-experts.

Numerical Probability
Numerical Probability

Author: Gilles Pagès

Publisher: Springer

Published: 2018-07-31

Total Pages: 591

ISBN-13: 3319902768

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This textbook provides a self-contained introduction to numerical methods in probability with a focus on applications to finance. Topics covered include the Monte Carlo simulation (including simulation of random variables, variance reduction, quasi-Monte Carlo simulation, and more recent developments such as the multilevel paradigm), stochastic optimization and approximation, discretization schemes of stochastic differential equations, as well as optimal quantization methods. The author further presents detailed applications to numerical aspects of pricing and hedging of financial derivatives, risk measures (such as value-at-risk and conditional value-at-risk), implicitation of parameters, and calibration. Aimed at graduate students and advanced undergraduate students, this book contains useful examples and over 150 exercises, making it suitable for self-study.

Fractal Geometry and Stochastics V
Fractal Geometry and Stochastics V

Author: Christoph Bandt

Publisher: Birkhäuser

Published: 2015-07-08

Total Pages: 339

ISBN-13: 3319186604

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This book collects significant contributions from the fifth conference on Fractal Geometry and Stochastics held in Tabarz, Germany, in March 2014. The book is divided into five topical sections: geometric measure theory, self-similar fractals and recurrent structures, analysis and algebra on fractals, multifractal theory, and random constructions. Each part starts with a state-of-the-art survey followed by papers covering a specific aspect of the topic. The authors are leading world experts and present their topics comprehensibly and attractively. Both newcomers and specialists in the field will benefit from this book.

Mathematical Modelling and Numerical Methods in Finance
Mathematical Modelling and Numerical Methods in Finance

Author: Alain Bensoussan

Publisher: Elsevier

Published: 2009-06-16

Total Pages: 743

ISBN-13: 0080931006

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Mathematical finance is a prolific scientific domain in which there exists a particular characteristic of developing both advanced theories and practical techniques simultaneously. Mathematical Modelling and Numerical Methods in Finance addresses the three most important aspects in the field: mathematical models, computational methods, and applications, and provides a solid overview of major new ideas and results in the three domains. - Coverage of all aspects of quantitative finance including models, computational methods and applications - Provides an overview of new ideas and results - Contributors are leaders of the field

Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data Science

Author: Giuseppe Nicosia

Publisher: Springer Nature

Published: 2022-02-01

Total Pages: 667

ISBN-13: 3030954676

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This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Applied Analysis, Optimization and Soft Computing
Applied Analysis, Optimization and Soft Computing

Author: Tanmoy Som

Publisher: Springer Nature

Published: 2023-06-10

Total Pages: 425

ISBN-13: 9819905974

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This book contains select contributions presented at the International Conference on Nonlinear Applied Analysis and Optimization (ICNAAO-2021), held at the Department of Mathematics Sciences, Indian Institute of Technology (BHU) Varanasi, India, from 21–23 December 2021. The book discusses topics in the areas of nonlinear analysis, fixed point theory, dynamical systems, optimization, fractals, applications to differential/integral equations, signal and image processing, and soft computing, and exposes the young talents with the newer dimensions in these areas with their practical approaches and to tackle the real-life problems in engineering, medical and social sciences. Scientists from the U.S.A., Austria, France, Mexico, Romania, and India have contributed their research. All the submissions are peer reviewed by experts in their fields.

Séminaire de Probabilités XLIII
Séminaire de Probabilités XLIII

Author: Catherine Donati Martin

Publisher: Springer

Published: 2010-10-20

Total Pages: 511

ISBN-13: 3642152171

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This is a new volume of the Séminaire de Probabilités which is now in its 43rd year. Following the tradition, this volume contains about 20 original research and survey articles on topics related to stochastic analysis. It contains an advanced course of J. Picard on the representation formulae for fractional Brownian motion. The regular chapters cover a wide range of themes, such as stochastic calculus and stochastic differential equations, stochastic differential geometry, filtrations, analysis on Wiener space, random matrices and free probability, as well as mathematical finance. Some of the contributions were presented at the Journées de Probabilités held in Poitiers in June 2009.

Monte Carlo and Quasi-Monte Carlo Methods 2006
Monte Carlo and Quasi-Monte Carlo Methods 2006

Author: Alexander Keller

Publisher: Springer Science & Business Media

Published: 2007-12-30

Total Pages: 684

ISBN-13: 3540744967

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This book presents the refereed proceedings of the Seventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, held in Ulm, Germany, in August 2006. The proceedings include carefully selected papers on many aspects of Monte Carlo and quasi-Monte Carlo methods and their applications. They also provide information on current research in these very active areas.

Principles of Nonparametric Learning
Principles of Nonparametric Learning

Author: Laszlo Györfi

Publisher: Springer

Published: 2014-05-04

Total Pages: 344

ISBN-13: 3709125685

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This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.