Approximating Integrals Via Monte Carlo And Deterministic Methods

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Approximating Integrals Via Monte Carlo and Deterministic Methods

Author : Michael John Evans
Publisher : Oxford University Press on Demand
ISBN 13 : 9780198502784
Total Pages : 288 pages
Book Rating : 4.5/5 (27 download)

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Book Synopsis Approximating Integrals Via Monte Carlo and Deterministic Methods by : Michael John Evans

Download or read book Approximating Integrals Via Monte Carlo and Deterministic Methods written by Michael John Evans and published by Oxford University Press on Demand. This book was released on 2000 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, and although the focus is on approximating higher- dimensional integrals thelower-dimensional case is also covered. Included in the book are asymptotic techniques, multiple quadrature and quasi-random techniques as well as a complete development of Monte Carlo algorithms. For the Monte Carlo section importance sampling methods, variance reduction techniques and the primaryMarkov Chain Monte Carlo algorithms are covered. This book brings these various techniques together for the first time, and hence provides an accessible textbook and reference for researchers in a wide variety of disciplines.

Approximating Integrals via Monte Carlo and Deterministic Methods

Author : Michael Evans
Publisher : OUP Oxford
ISBN 13 : 019158987X
Total Pages : 302 pages
Book Rating : 4.1/5 (915 download)

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Book Synopsis Approximating Integrals via Monte Carlo and Deterministic Methods by : Michael Evans

Download or read book Approximating Integrals via Monte Carlo and Deterministic Methods written by Michael Evans and published by OUP Oxford. This book was released on 2000-03-23 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, and although the focus is on approximating higher- dimensional integrals the lower-dimensional case is also covered. Included in the book are asymptotic techniques, multiple quadrature and quasi-random techniques as well as a complete development of Monte Carlo algorithms. For the Monte Carlo section importance sampling methods, variance reduction techniques and the primary Markov Chain Monte Carlo algorithms are covered. This book brings these various techniques together for the first time, and hence provides an accessible textbook and reference for researchers in a wide variety of disciplines.

Approximation of Integrals Via Monte Carlo Methods, with an Application to Calculating Radar Detection Probabilities

Author : Graham V. Weinberg
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (224 download)

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Book Synopsis Approximation of Integrals Via Monte Carlo Methods, with an Application to Calculating Radar Detection Probabilities by : Graham V. Weinberg

Download or read book Approximation of Integrals Via Monte Carlo Methods, with an Application to Calculating Radar Detection Probabilities written by Graham V. Weinberg and published by . This book was released on 2005 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Approximation of Integrals Via Monte Carlo Methods, With an Applications to Calculating Radar Detection Probabilities

Author :
Publisher :
ISBN 13 :
Total Pages : 39 pages
Book Rating : 4.:/5 (644 download)

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Book Synopsis Approximation of Integrals Via Monte Carlo Methods, With an Applications to Calculating Radar Detection Probabilities by :

Download or read book Approximation of Integrals Via Monte Carlo Methods, With an Applications to Calculating Radar Detection Probabilities written by and published by . This book was released on 2005 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: The approximation of definite integrals using Monte Carlo simulations is the focus of the work presented here. The general methodology of estimation by sampling is introduced, and is applied to the approximation of two special functions of mathematics: the Gamma and Beta functions. A significant application, in the context of radar detection theory, is based upon the work of Shnidman 1998. The latter considers problems associated with the optimal choice of binary integration parameters. We apply the techniques of Monte Carlo simulation to estimate binary integration detection probabilities.

Monte Carlo and Quasi-Monte Carlo Methods 2012

Author : Josef Dick
Publisher : Springer Science & Business Media
ISBN 13 : 3642410952
Total Pages : 680 pages
Book Rating : 4.6/5 (424 download)

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Book Synopsis Monte Carlo and Quasi-Monte Carlo Methods 2012 by : Josef Dick

Download or read book Monte Carlo and Quasi-Monte Carlo Methods 2012 written by Josef Dick and published by Springer Science & Business Media. This book was released on 2013-12-05 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book represents the refereed proceedings of the Tenth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of New South Wales (Australia) in February 2012. These biennial conferences are major events for Monte Carlo and the premiere event for quasi-Monte Carlo research. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. The reader will be provided with information on latest developments in these very active areas. The book is an excellent reference for theoreticians and practitioners interested in solving high-dimensional computational problems arising, in particular, in finance, statistics and computer graphics.

Introducing Monte Carlo Methods with R

Author : Christian Robert
Publisher : Springer Science & Business Media
ISBN 13 : 1441915753
Total Pages : 297 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Introducing Monte Carlo Methods with R by : Christian Robert

Download or read book Introducing Monte Carlo Methods with R written by Christian Robert and published by Springer Science & Business Media. This book was released on 2010 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Stochastic Analysis 2010

Author : Dan Crisan
Publisher : Springer Science & Business Media
ISBN 13 : 3642153585
Total Pages : 303 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Stochastic Analysis 2010 by : Dan Crisan

Download or read book Stochastic Analysis 2010 written by Dan Crisan and published by Springer Science & Business Media. This book was released on 2010-11-26 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Analysis aims to provide mathematical tools to describe and model high dimensional random systems. Such tools arise in the study of Stochastic Differential Equations and Stochastic Partial Differential Equations, Infinite Dimensional Stochastic Geometry, Random Media and Interacting Particle Systems, Super-processes, Stochastic Filtering, Mathematical Finance, etc. Stochastic Analysis has emerged as a core area of late 20th century Mathematics and is currently undergoing a rapid scientific development. The special volume “Stochastic Analysis 2010” provides a sample of the current research in the different branches of the subject. It includes the collected works of the participants at the Stochastic Analysis section of the 7th ISAAC Congress organized at Imperial College London in July 2009.

Monte Carlo and Quasi-Monte Carlo Methods 2000

Author : Kai-Tai Fang
Publisher : Springer Science & Business Media
ISBN 13 : 3642560466
Total Pages : 570 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Monte Carlo and Quasi-Monte Carlo Methods 2000 by : Kai-Tai Fang

Download or read book Monte Carlo and Quasi-Monte Carlo Methods 2000 written by Kai-Tai Fang and published by Springer Science & Business Media. This book was released on 2011-06-28 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book represents the refereed proceedings of the Fourth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing which was held at Hong Kong Baptist University in 2000. An important feature are invited surveys of the state-of-the-art in key areas such as multidimensional numerical integration, low-discrepancy point sets, random number generation, and applications of Monte Carlo and quasi-Monte Carlo methods. These proceedings include also carefully selected contributed papers on all aspects of Monte Carlo and quasi-Monte Carlo methods. The reader will be informed about current research in this very active field.

Lectures on Monte Carlo Methods

Author : Neal Noah Madras
Publisher : American Mathematical Soc.
ISBN 13 : 0821829785
Total Pages : 113 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Lectures on Monte Carlo Methods by : Neal Noah Madras

Download or read book Lectures on Monte Carlo Methods written by Neal Noah Madras and published by American Mathematical Soc.. This book was released on 2002 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo methods form an experimental branch of mathematics that employs simulations driven by random number generators. These methods are often used when others fail, since they are much less sensitive to the ``curse of dimensionality'', which plagues deterministic methods in problems with a large number of variables. Monte Carlo methods are used in many fields: mathematics, statistics, physics, chemistry, finance, computer science, and biology, for instance. This book is an introduction to Monte Carlo methods for anyone who would like to use these methods to study various kinds of mathematical models that arise in diverse areas of application. The book is based on lectures in a graduate course given by the author. It examines theoretical properties of Monte Carlo methods as well as practical issues concerning their computer implementation and statistical analysis. The only formal prerequisite is an undergraduate course in probability. The book is intended to be accessible to students from a wide range of scientific backgrounds. Rather than being a detailed treatise, it covers the key topics of Monte Carlo methods to the depth necessary for a researcher to design, implement, and analyze a full Monte Carlo study of a mathematical or scientific problem. The ideas are illustrated with diverse running examples. There are exercises sprinkled throughout the text. The topics covered include computer generation of random variables, techniques and examples for variance reduction of Monte Carlo estimates, Markov chain Monte Carlo, and statistical analysis of Monte Carlo output.

Uncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics

Author : Sunetra Sarkar
Publisher : World Scientific
ISBN 13 : 9814730599
Total Pages : 197 pages
Book Rating : 4.8/5 (147 download)

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Book Synopsis Uncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics by : Sunetra Sarkar

Download or read book Uncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics written by Sunetra Sarkar and published by World Scientific. This book was released on 2016-08-18 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decade, research in Uncertainty Quantification (UC) has received a tremendous boost, in fluid engineering and coupled structural-fluids systems. New algorithms and adaptive variants have also emerged.This timely compendium overviews in detail the current state of the art of the field, including advances in structural engineering, along with the recent focus on fluids and coupled systems. Such a strong compilation of these vibrant research areas will certainly be an inspirational reference material for the scientific community.

Random Number Generation and Monte Carlo Methods

Author : James E. Gentle
Publisher : Springer Science & Business Media
ISBN 13 : 0387216103
Total Pages : 387 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Random Number Generation and Monte Carlo Methods by : James E. Gentle

Download or read book Random Number Generation and Monte Carlo Methods written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random variates from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments. The book includes exercises and can be used as a test or supplementary text for various courses in modern statistics. It could serve as the primary test for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, could also serve as a useful reference for practitioners. Although some familiarity with probability and statistics is assumed, the book is accessible to a broad audience. The second edition is approximately 50% longer than the first edition. It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

Monte Carlo Methods for Applied Scientists

Author : Ivan Dimov
Publisher : World Scientific
ISBN 13 : 9812779892
Total Pages : 308 pages
Book Rating : 4.8/5 (127 download)

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Book Synopsis Monte Carlo Methods for Applied Scientists by : Ivan Dimov

Download or read book Monte Carlo Methods for Applied Scientists written by Ivan Dimov and published by World Scientific. This book was released on 2008 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer. This book attempts to bridge the gap between theory and practice concentrating on modern algorithmic implementation on parallel architecture machines. Although a suitable text for final year postgraduate mathematicians and computational scientists it is principally aimed at the applied scientists: only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithms development often to applied industrial problems. A selection of algorithms developed both for serial and parallel machines are provided. Sample Chapter(s). Chapter 1: Introduction (231 KB). Contents: Basic Results of Monte Carlo Integration; Optimal Monte Carlo Method for Multidimensional Integrals of Smooth Functions; Iterative Monte Carlo Methods for Linear Equations; Markov Chain Monte Carlo Methods for Eigenvalue Problems; Monte Carlo Methods for Boundary-Value Problems (BVP); Superconvergent Monte Carlo for Density Function Simulation by B-Splines; Solving Non-Linear Equations; Algorithmic Effciency for Different Computer Models; Applications for Transport Modeling in Semiconductors and Nanowires. Readership: Applied scientists and mathematicians.

Data Analysis from Statistical Foundations

Author : Donald Alexander Stuart Fraser
Publisher : Nova Publishers
ISBN 13 : 9781560729686
Total Pages : 442 pages
Book Rating : 4.7/5 (296 download)

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Book Synopsis Data Analysis from Statistical Foundations by : Donald Alexander Stuart Fraser

Download or read book Data Analysis from Statistical Foundations written by Donald Alexander Stuart Fraser and published by Nova Publishers. This book was released on 2001 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis from Statistical Foundations

Computation of Multivariate Normal and t Probabilities

Author : Alan Genz
Publisher : Springer Science & Business Media
ISBN 13 : 3642016898
Total Pages : 130 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Computation of Multivariate Normal and t Probabilities by : Alan Genz

Download or read book Computation of Multivariate Normal and t Probabilities written by Alan Genz and published by Springer Science & Business Media. This book was released on 2009-07-09 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.

Surrogate Model-Based Engineering Design and Optimization

Author : Ping Jiang
Publisher : Springer Nature
ISBN 13 : 9811507317
Total Pages : 240 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Surrogate Model-Based Engineering Design and Optimization by : Ping Jiang

Download or read book Surrogate Model-Based Engineering Design and Optimization written by Ping Jiang and published by Springer Nature. This book was released on 2019-11-01 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers some of the most popular methods in design space sampling, ensembling surrogate models, multi-fidelity surrogate model construction, surrogate model selection and validation, surrogate-based robust design optimization, and surrogate-based evolutionary optimization. Surrogate or metamodels are now frequently used in complex engineering product design to replace expensive simulations or physical experiments. They are constructed from available input parameter values and the corresponding output performance or quantities of interest (QOIs) to provide predictions based on the fitted or interpolated mathematical relationships. The book highlights a range of methods for ensembling surrogate and multi-fidelity models, which offer a good balance between surrogate modeling accuracy and building cost. A number of real-world engineering design problems, such as three-dimensional aircraft design, are also provided to illustrate the ability of surrogates for supporting complex engineering design. Lastly, illustrative examples are included throughout to help explain the approaches in a more “hands-on” manner.

Numerical Methods for Nonlinear Estimating Equations

Author : Christopher G. Small
Publisher : Oxford University Press
ISBN 13 : 9780198506881
Total Pages : 330 pages
Book Rating : 4.5/5 (68 download)

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Book Synopsis Numerical Methods for Nonlinear Estimating Equations by : Christopher G. Small

Download or read book Numerical Methods for Nonlinear Estimating Equations written by Christopher G. Small and published by Oxford University Press. This book was released on 2003 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihood's for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modification to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student.

Integrated Tracking, Classification, and Sensor Management

Author : Mahendra Mallick
Publisher : John Wiley & Sons
ISBN 13 : 0470639059
Total Pages : 738 pages
Book Rating : 4.4/5 (76 download)

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Book Synopsis Integrated Tracking, Classification, and Sensor Management by : Mahendra Mallick

Download or read book Integrated Tracking, Classification, and Sensor Management written by Mahendra Mallick and published by John Wiley & Sons. This book was released on 2012-12-03 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique guide to the state of the art of tracking, classification, and sensor management This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solving A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management Insight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR) With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.