Essentials Of Monte Carlo Simulation

Author: Nick T. Thomopoulos
Publisher: Springer Science & Business Media
ISBN: 1461460220
Size: 58.18 MB
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Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics.

Modeling Risk Dvd

Author: Johnathan Mun
Publisher: John Wiley & Sons
ISBN: 0470620013
Size: 55.74 MB
Format: PDF, Kindle
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An updated guide to risk analysis and modeling Although risk was once seen as something that was both unpredictable and uncontrollable, the evolution of risk analysis tools and theories has changed the way we look at this important business element. In the Second Edition of Analyzing and Modeling Risk, expert Dr. Johnathan Mun provides up-to-date coverage of risk analysis as it is applied within the realms of business risk analysis and offers an intuitive feel of what risk looks like, as well as the different ways of quantifying it. This Second Edition provides professionals in all industries a more comprehensive guide on such key concepts as risk and return, the fundamentals of model building, Monte Carlo simulation, forecasting, time-series and regression analysis, optimization, real options, and more. Includes new examples, questions, and exercises as well as updates using Excel 2007 Book supported by author's proprietary risk analysis software found on the companion CD-ROM Offers both a qualitative and quantitative description of risk Filled with in-depth insights and practical advice, this reliable resource covers all of the essential tools and techniques that risk managers need to successfully conduct risk analysis. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Handbook Of Research On Data Science For Effective Healthcare Practice And Administration

Author: Noughabi, Elham Akhond Zadeh
Publisher: IGI Global
ISBN: 1522525165
Size: 28.17 MB
Format: PDF, ePub
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Data science has always been an effective way of extracting knowledge and insights from information in various forms. One industry that can utilize the benefits from the advances in data science is the healthcare field. The Handbook of Research on Data Science for Effective Healthcare Practice and Administration is a critical reference source that overviews the state of data analysis as it relates to current practices in the health sciences field. Covering innovative topics such as linear programming, simulation modeling, network theory, and predictive analytics, this publication is recommended for all healthcare professionals, graduate students, engineers, and researchers that are seeking to expand their knowledge of efficient techniques for information analysis in the healthcare professions.

Designing Value Creating Supply Chain Networks

Author: Alain Martel
Publisher: Springer
ISBN: 3319281461
Size: 47.47 MB
Format: PDF, ePub
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Focusing on the design of robust value-creating supply chain networks (SCN) and key strategic issues related to the number; location, capacity and mission of supply chain facilities (plants, distribution centers) – as well as the network structure required to provide flexibility and resilience in an uncertain world – this book presents an innovative methodology for SCN reengineering that can be used to significantly improve the bottom line of supply chain dependent businesses. Providing readers with the tools needed to analyze and model value creation activities, Designing Value-Creating Supply Chain Networks examines the risks faced by modern supply chains, and shows how to develop plausible future scenarios to evaluate potential SCN designs. The design methods proposed are based on a visual representation formalism that facilitates the analysis and modeling of SCN design problems, book chapters incorporate several example problems and exercises which can be solved with Excel tools (Analysis tools and Solver) or with commercial statistical and optimization software. Winner of the 2016 Coup de Coeur prize at the Plumes des Achats & Supply Chain, Paris.

Advances In Computational Environment Science

Author: Gary Lee
Publisher: Springer Science & Business Media
ISBN: 3642279570
Size: 56.17 MB
Format: PDF, ePub, Docs
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2012 International Conference on Environment Science and 2012 International Conference on Computer Science (ICES 2012/ICCS 2012) will be held in Australia, Melbourne, 15‐16 March, 2012.Volume 1 contains some new results in computational environment science. There are 47 papers were selected as the regular paper in this volume. It contains the latest developments and reflects the experience of many researchers working in different environments (universities, research centers or even industries), publishing new theories and solving new technological problems on computational environment science. The purpose of volume 1 is interconnection of diverse scientific fields, the cultivation of every possible scientific collaboration, the exchange of views and the promotion of new research targets as well as the further dissemination, the dispersion, the diffusion of the environment science, including but not limited to Ecology, Physics, Chemistry, Biology, Soil Science, Geology, Atmospheric Science and Geography We are sure that the efforts of the authors as well as the reviewers to provide high level contributions will be appreciated by the relevant scientific community. We are convinced that presented volume will be a source of knowledge and inspiration for all academic members, researchers and practitioners working in a field of the topic covered by the book.

Managing Project Risks For Competitive Advantage In Changing Business Environments

Author: Bodea, Constanta-Nicoleta
Publisher: IGI Global
ISBN: 1522503366
Size: 41.96 MB
Format: PDF, Kindle
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Risk management is a vital concern in any organization. In order to succeed in the competitive modern business environment, the decision-making process must be effectively governed and managed. Managing Project Risks for Competitive Advantage in Changing Business Environments presents critical discussions on effective risk management in projects and methods to ensure overall success in project outcomes. Highlighting theoretical foundations, innovative practices, and real-world applications, this book is a pivotal reference source for managers, practitioners, upper-level students, and other professionals interested in how to properly adopt project risk management systems and tools.

Empirical Model Building

Author: James R. Thompson
Publisher: John Wiley & Sons
ISBN: 1118109627
Size: 70.88 MB
Format: PDF, Mobi
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Praise for the First Edition "This...novel and highly stimulating book, which emphasizes solving real problems...should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general." - Short Book Reviews This new edition features developments and real-world examples that showcase essential empirical modeling techniques Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists. The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition now features further coverage of momentum based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized and newly added and updated exercises allow readers to test their comprehension of the presented material. Empirical Model Building, Second Edition is a suitable book for modeling courses at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.

Introduction To Scientific Programming And Simulation Using R Second Edition

Author: Owen Jones
Publisher: CRC Press
ISBN: 1466570016
Size: 19.19 MB
Format: PDF, ePub, Docs
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Learn How to Program Stochastic Models Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data. The book’s four parts teach: Core knowledge of R and programming concepts How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation Essentials of probability, random variables, and expectation required to understand simulation Stochastic modelling and simulation, including random number generation and Monte Carlo integration In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size. Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables. Building readers’ statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.

Introduction To Time Series Modeling

Author: Genshiro Kitagawa
Publisher: CRC Press
ISBN: 9781584889229
Size: 25.36 MB
Format: PDF, Kindle
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In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the various tools for handling them. The book employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods, including the Kalman filter, the non-Gaussian filter, and the sequential Monte Carlo filter, for the state-space models. Taking a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike, the author derives various methods of parameter estimation, such as the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by the Akaike information criterion (AIC). Along with simulation methods, he also covers standard stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model. With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It encourages readers to build models for their own real-life problems.