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Simulation

Simulation Author Sheldon M. Ross
ISBN-10 9780080517223
Release 2006-08-01
Pages 312
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Ross's Simulation, Fourth Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model. New to this Edition: -More focus on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis -A chapter on Markov chain monte carlo methods with many examples -Unique material on the alias method for generating discrete random variables



Decision Sciences

Decision Sciences Author Raghu Nandan Sengupta
ISBN-10 9781482282566
Release 2016-11-30
Pages 1026
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This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.



Regenerative Stochastic Simulation

Regenerative Stochastic Simulation Author Gerald S. Shedler
ISBN-10 9780080925721
Release 1992-12-17
Pages 400
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Simulation is a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic and/or numerical techniques do not suffice. The focus of this book is on simulations of discrete-event stochastic systems; namely, simulations in which stochastic state transitions occur only at an increasing sequence of random times. The discussion emphasizes simulations on a finite or countably infinite state space. * Develops probabilistic methods for simulation of discrete-event stochastic systems * Emphasizes stochastic modeling and estimation procedures based on limit theorems for regenerative stochastic processes * Includes engineering applications of discrete-even simulation to computer, communication, manufacturing, and transportation systems * Focuses on simulations with an underlying stochastic process that can specified as a generalized semi-Markov process * Unique approach to simulation, with heavy emphasis on stochastic modeling * Includes engineering applications for computer, communication, manufacturing, and transportation systems



Modeling for All Scales

Modeling for All Scales Author Howard T. Odum
ISBN-10 0080544363
Release 2000-02-03
Pages 458
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All manner of models are used to describe, simulate, extrapolate, and ultimately understand the function of dynamic systems. These sorts of models are usually based upon a mathematical foundation that can be difficult to manipulate especially for students. Modeling for All Scales uses object-oriented programming to erect and evaluate the efficacy of models of small, intermediate and large scale systems. Such models allow users to employ intuitively based symbols and a systems ecology approach. The authors have been leaders in the systems ecology community and have originated much of the scientific vocabulary of the field. After introducing modeling and its benefits, there is a series of chapters detailing the more particular elements of successful simulation. There follows another series of chapters, each devoted to models of different sorts of systems. Small scale models of growth, competition, and evolution give way, successively, to larger and larger scale models such as international trade and the global geobiosphere. Anyone interested in an easy to use approach to modeling complex systems authored by perhaps the most original systems ecologists of the century will want this book. To further enhance the users ability to apply the lessons of this book, there is included a CD-ROM disc which provides the fundamental tools for modeling at all scales. Key Features * The book makes it possible to teach modeling and simulation without much prior knowledge of mathematics * Reasons for modeling and simulation are discussed * The book makes modeling and simulation fun by keeping focused on simplified overview minimodels that have important principles to science and society * The steps in successive chapters are arranged so that readers can teach themselves modeling, simulation, and the programming necessary to simulate the systems they diagram * The CD-ROM has minimodel programs and versions of QuickBasic and EXTEND to run them



PROBABILITY AND STATISTICS Volume II

PROBABILITY AND STATISTICS   Volume II Author Reinhard Viertl
ISBN-10 9781848260535
Release 2009-06-11
Pages 520
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Probability and Statistics theme is a component of Encyclopedia of Mathematical Sciences in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. The Theme with contributions from distinguished experts in the field, discusses Probability and Statistics. Probability is a standard mathematical concept to describe stochastic uncertainty. Probability and Statistics can be considered as the two sides of a coin. They consist of methods for modeling uncertainty and measuring real phenomena. Today many important political, health, and economic decisions are based on statistics. This theme is structured in five main topics: Probability and Statistics; Probability Theory; Stochastic Processes and Random Fields; Probabilistic Models and Methods; Foundations of Statistics, which are then expanded into multiple subtopics, each as a chapter. These three volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.



Data Analysis and Decision Making

Data Analysis and Decision Making Author S. Christian Albright
ISBN-10 9781133171553
Release 2010-10-12
Pages 1080
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DATA ANALYSIS AND DECISION MAKING emphasizes data analysis, modeling, and spreadsheet use in statistics and management science. This text became a market leader in its first edition for its clarity of writing and teach-by-example approach, and it continues that tradition in this edition. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



Optimization Techniques in Statistics

Optimization Techniques in Statistics Author Jagdish S. Rustagi
ISBN-10 9781483295718
Release 2014-05-19
Pages 359
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Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates Markov decision processes Programming methods used to optimize monitoring of patients in hospitals Derivation of the Neyman-Pearson lemma The search for optimal designs Simulation of a steel mill Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. Key Features * Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing * Develops a wide range of statistical techniques in the unified context of optimization * Discusses applications such as optimizing monitoring of patients and simulating steel mill operations * Treats numerical methods and applications Includes exercises and references for each chapter * Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization



Introduction to Business Analytics Using Simulation

Introduction to Business Analytics Using Simulation Author Jonathan P. Pinder
ISBN-10 9780128104866
Release 2016-09-03
Pages 448
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Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. Winner of the 2017 Textbook and Academic Authors Association (TAA) Most Promising New Textbook Award. Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making Explains the processes needed to develop, report, and analyze business data Describes how to use and apply business analytics software



Assessing the Reliability of Complex Models

Assessing the Reliability of Complex Models Author Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification
ISBN-10 9780309256346
Release 2012-06-26
Pages 131
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Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners.



Statistics Data Analysis and Decision Modeling

Statistics  Data Analysis  and Decision Modeling Author James Robert Evans
ISBN-10 0131886096
Release 2007
Pages 557
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This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made.Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models.For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions.



Breakthroughs in Decision Science and Risk Analysis

Breakthroughs in Decision Science and Risk Analysis Author Louis A. Cox
ISBN-10 9781118217160
Release 2015-03-30
Pages 328
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Discover recent powerful advances in the theory, methods, and applications of decision and risk analysis Focusing on recent advances and innovations in the field of decision analysis (DA), Breakthroughs in Decision Science and Risk Analysis presents theories and methods for making, improving, and learning from significant practical decisions. The book explains these new methods and important applications in an easily accessible and stimulating style for readers from multiple backgrounds, including psychology, economics, statistics, engineering, risk analysis, operations research, and management science. Emphasizing topics not traditionally found in DA literature, the book illustrates genuine advances in practical decision science and includes developments and trends that depart from, or break with, the standard axiomatic DA paradigm in fundamental and useful ways. The book features methods for coping with realistic decision-making challenges, including online adaptive learning algorithms, innovations in robust decision-making, and the use of a variety of models to provide more-or-less plausible explanations for available data and recommended actions. In addition, the book illustrates how these techniques can be applied to dramatically improve risk management decisions. Breakthroughs in Decision Science and Risk Analysis also includes: An emphasis on new approaches rather than classical and traditional ideas Discussions of how decision and risk analysis can be applied to improve high-stakes policy and management decisions Coverage of the potential value and realism of decision science within applications in financial, health, safety, environmental, business, engineering, and security risk management Innovative methods for deciding what to do when decision problems are not completely known or described or when useful probabilities cannot be specified Recent breakthroughs in the psychology and brain science of risky decisions, mathematical foundations and techniques, and integration with learning and pattern recognition methods from computational intelligence Breakthroughs in Decision Science and Risk Analysis is an ideal reference for researchers, consultants, and practitioners in the fields of decision science, operations research, business, management science, engineering, statistics, and mathematics. The book is also an appropriate guide for managers, analysts, and decision and policy makers in the areas of finance, health and safety, environment, business, engineering, and security risk management. Louis Anthony (Tony) Cox, Jr. PhD, is Chief Sciences Officer of NextHealth Technologies, a Denver-based health care advanced analytics software company and President of Cox Associates, Inc., a Denver-based applied research company specializing in quantitative health risk assessment, risk analysis, causal modeling, and operations research. Dr. Cox is also Clinical Professor of Biostatistics and Informatics at the University of Colorado at Denver, a member of the National Academy of Engineering, and Editor-in-Chief of Risk Analysis: An International Journal.



Data Analysis Optimization and Simulation Modeling

Data Analysis  Optimization  and Simulation Modeling Author S. Christian Albright
ISBN-10 0538476761
Release 2011
Pages 1061
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DATA ANALYSIS, OPTIMIZATION, AND SIMULATION MODELING, 4e, International Edition is a teach-by-example approach, learner-friendly writing style, and complete Excel integration focusing on data analysis, modeling, and spreadsheet use in statistics and management science. The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis. All of the add-ins is revised for Excel 2007 and notes about Excel 2010 are added where applicable.



Business Analytics with Management Science Models and Methods

Business Analytics with Management Science Models and Methods Author Arben Asllani
ISBN-10 9780133760668
Release 2014-11-17
Pages 400
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Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics. Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.



Modeling and Simulation in the Medical and Health Sciences

Modeling and Simulation in the Medical and Health Sciences Author John A. Sokolowski
ISBN-10 9781118003190
Release 2012-01-25
Pages 304
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This edited book is divided into three parts: Fundamentals of Medical and Health Sciences Modeling and Simulation introduces modeling and simulation in the medical and health sciences; Medical and Health Sciences Models provides the theoretical underpinnings of medical and health sciences modeling; and Modeling and Simulation Applications in Medical and Health Sciences focuses on teaching, training, and research applications. The book begins with a general discussion of modeling and simulation from the modeling and simulation discipline perspective. This discussion grounds the reader in common terminology. It also relates this terminology to concepts found in the medical and health care (MHC) area to help bridge the gap between developers and MHC practitioners. Three distinct modes of modeling and simulation are described: live, constructive, and virtual. The live approach explains the concept of using real (live) people employing real equipment for training purposes. The constructive mode is a means of engaging medical modeling and simulation. In constructive simulation, simulated people and simulated equipment are developed to augment real-world conditions for training or experimentation purposes. The virtual mode is perhaps the most fascinating as virtual operating rooms and synthetic training environments are being produced for practitioners and educators at break-neck speed. In this mode, real people are employing simulated equipment to improve physical skills and decision-making ability.



Simulation for Data Science with R

Simulation for Data Science with R Author Matthias Templ
ISBN-10 9781785885877
Release 2016-06-30
Pages 398
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Harness actionable insights from your data with computational statistics and simulations using R About This Book Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulation Who This Book Is For This book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required. What You Will Learn The book aims to explore advanced R features to simulate data to extract insights from your data. Get to know the advanced features of R including high-performance computing and advanced data manipulation See random number simulation used to simulate distributions, data sets, and populations Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations Applications to design statistical solutions with R for solving scientific and real world problems Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more. In Detail Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems. Style and approach This book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these methods, and provides computational tools to help you solve common problems in statistical simulation and computer-intense methods.



Spreadsheet Modeling Decision Analysis A Practical Introduction to Business Analytics

Spreadsheet Modeling   Decision Analysis  A Practical Introduction to Business Analytics Author Cliff Ragsdale
ISBN-10 9781305947412
Release 2016-12-05
Pages 864
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Written by an innovator in teaching spreadsheets and a highly regarded leader in business analytics, Cliff Ragsdale’s SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 8E helps readers master important spreadsheet and business analytics skills. Readers find everything needed to become proficient in today’s most widely used business analytics techniques using Microsoft Office Excel 2016. Learning to make effective decisions in today's business world takes training and experience. Author Cliff Ragsdale guides learners through the skills needed, using the latest Excel for Windows. Readers apply what they’ve learned to real business situations with step-by-step instructions and annotated screen images that make examples easy to follow. The World of Management Science sections further demonstrates how each topic applies to a real company. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



Decision Science and Social Risk Management

Decision Science and Social Risk Management Author M.W Merkhofer
ISBN-10 9789400946989
Release 2012-12-06
Pages 330
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Economists, decision analysts, management scientists, and others have long argued that government should take a more scientific approach to decision making. Pointing to various theories for prescribing and rational izing choices, they have maintained that social goals could be achieved more effectively and at lower costs if government decisions were routinely subjected to analysis. Now, government policy makers are putting decision science to the test. Recent government actions encourage and in some cases require government decisions to be evaluated using formally defined principles 01' rationality. Will decision science pass tbis test? The answer depends on whether analysts can quickly and successfully translate their theories into practical approaches and whether these approaches promote the solution of the complex, highly uncertain, and politically sensitive problems that are of greatest concern to government decision makers. The future of decision science, perhaps even the nation's well-being, depends on the outcome. A major difficulty for the analysts who are being called upon by government to apply decision-aiding approaches is that decision science has not yet evolved a universally accepted methodology for analyzing social decisions involving risk. Numerous approaches have been proposed, including variations of cost-benefit analysis, decision analysis, and applied social welfare theory. Each of these, however, has its limitations and deficiencies and none has a proven track record for application to govern ment decisions involving risk. Cost-benefit approaches have been exten sively applied by the government, but most applications have been for decisions that were largely risk-free.