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Author : G. Arthur Mihram
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.14/5 ( download)
Download or read book Simulation written by G. Arthur Mihram and published by . This book was released on 1972 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Barry L. Nelson
Publisher : Springer Nature
ISBN 13 : 3030861945
Total Pages : 323 pages
Book Rating : 4.40/5 ( download)
Download or read book Foundations and Methods of Stochastic Simulation written by Barry L. Nelson and published by Springer Nature. This book was released on 2021-11-10 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the mathematical and statistical foundations of simulation and why it works. The book is rigorous and complete, but concise and accessible, providing all necessary background material. Object-oriented programming of simulations is illustrated in Python, while the majority of the book is programming language independent. In addition to covering the foundations of simulation and simulation programming for applications, the text prepares readers to use simulation in their research. A solutions manual for end-of-chapter exercises is available for instructors.
Author : G. Arthur Mihram
Publisher : Elsevier Science & Technology
ISBN 13 : 9780124959507
Total Pages : 546 pages
Book Rating : 4.04/5 ( download)
Download or read book Simulation: Statistical Foundations and Methodology written by G. Arthur Mihram and published by Elsevier Science & Technology. This book was released on 1972 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models; The stochastic variate; Time series; The Monte Carlo methods; Modeling; Fundamentals of simular experimentation; Experimental design for qualitative factors; Experimental design for quanatitative factors; The search for optimal system conditions; The simulation as a stochastic process; Vistas in simulation.
Author :
Publisher : Academic Press
ISBN 13 : 9780080956015
Total Pages : 322 pages
Book Rating : 4.17/5 ( download)
Download or read book Simulation Statistical Foundations and Methodology written by and published by Academic Press. This book was released on 1972-09-29 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering
Author : Barry Nelson
Publisher : Springer Science & Business Media
ISBN 13 : 146146160X
Total Pages : 285 pages
Book Rating : 4.09/5 ( download)
Download or read book Foundations and Methods of Stochastic Simulation written by Barry Nelson and published by Springer Science & Business Media. This book was released on 2013-01-31 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material); and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems and prepares the reader to use simulation in research as well as practice. The book is a rigorous, but concise treatment, emphasizing lasting principles but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this. An online solutions manual for end of chapter exercises is also provided.
Author : Shravan Vasishth
Publisher : Springer Science & Business Media
ISBN 13 : 3642163130
Total Pages : 187 pages
Book Rating : 4.35/5 ( download)
Download or read book The Foundations of Statistics: A Simulation-based Approach written by Shravan Vasishth and published by Springer Science & Business Media. This book was released on 2010-11-11 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics and hypothesis testing are routinely used in areas (such as linguistics) that are traditionally not mathematically intensive. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on. As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research — they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided (the freely available programming language R is used throughout). Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US. ”Vasishth and Broe have written an attractive introduction to the foundations of statistics. It is concise, surprisingly comprehensive, self-contained and yet quite accessible. Highly recommended.” Harald Baayen, Professor of Linguistics, University of Alberta, Canada ”By using the text students not only learn to do the specific things outlined in the book, they also gain a skill set that empowers them to explore new areas that lie beyond the book’s coverage.” Colin Phillips, Professor of Linguistics, University of Maryland, USA
Author : Mark Tuckerman
Publisher : OUP Oxford
ISBN 13 : 0191523461
Total Pages : 719 pages
Book Rating : 4.65/5 ( download)
Download or read book Statistical Mechanics: Theory and Molecular Simulation written by Mark Tuckerman and published by OUP Oxford. This book was released on 2010-02-11 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex systems that bridge the traditional disciplines of physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computers. The aim of this book is to prepare burgeoning users and developers to become active participants in this exciting and rapidly advancing research area by uniting for the first time, in one monograph, the basic concepts of equilibrium and time-dependent statistical mechanics with the modern techniques used to solve the complex problems that arise in real-world applications. The book contains a detailed review of classical and quantum mechanics, in-depth discussions of the most commonly used ensembles simultaneously with modern computational techniques such as molecular dynamics and Monte Carlo, and important topics including free-energy calculations, linear-response theory, harmonic baths and the generalized Langevin equation, critical phenomena, and advanced conformational sampling methods. Burgeoning users and developers are thus provided firm grounding to become active participants in this exciting and rapidly advancing research area, while experienced practitioners will find the book to be a useful reference tool for the field.
Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 1466510854
Total Pages : 752 pages
Book Rating : 4.52/5 ( download)
Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author : Kurt Binder
Publisher : Springer Science & Business Media
ISBN 13 : 366230273X
Total Pages : 201 pages
Book Rating : 4.36/5 ( download)
Download or read book Monte Carlo Simulation in Statistical Physics written by Kurt Binder and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: When learning very formal material one comes to a stage where one thinks one has understood the material. Confronted with a "realiife" problem, the passivity of this understanding sometimes becomes painfully elear. To be able to solve the problem, ideas, methods, etc. need to be ready at hand. They must be mastered (become active knowledge) in order to employ them successfully. Starting from this idea, the leitmotif, or aim, of this book has been to elose this gap as much as possible. How can this be done? The material presented here was born out of a series of lectures at the Summer School held at Figueira da Foz (Portugal) in 1987. The series of lectures was split into two concurrent parts. In one part the "formal material" was presented. Since the background of those attending varied widely, the presentation of the formal material was kept as pedagogic as possible. In the formal part the general ideas behind the Monte Carlo method were developed. The Monte Carlo method has now found widespread appli cation in many branches of science such as physics, chemistry, and biology. Because of this, the scope of the lectures had to be narrowed down. We could not give a complete account and restricted the treatment to the ap plication of the Monte Carlo method to the physics of phase transitions. Here particular emphasis is placed on finite-size effects.
Author : Ding-Geng (Din) Chen
Publisher : Springer
ISBN 13 : 9811033072
Total Pages : 430 pages
Book Rating : 4.70/5 ( download)
Download or read book Monte-Carlo Simulation-Based Statistical Modeling written by Ding-Geng (Din) Chen and published by Springer. This book was released on 2017-02-01 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
