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Optimization Techniques In Statistics

Author: Jagdish S. Rustagi
Publisher: Elsevier
ISBN: 1483295710
Size: 39.47 MB
Format: PDF, Mobi
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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

Optimization And Decision Science Methodologies And Applications

Author: Antonio Sforza
Publisher: Springer
ISBN: 3319673084
Size: 71.98 MB
Format: PDF
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This proceedings volume highlights the state-of-the-art knowledge related to optimization, decisions science and problem solving methods, as well as their application in industrial and territorial systems. It includes contributions tackling these themes using models and methods based on continuous and discrete optimization, network optimization, simulation and system dynamics, heuristics, metaheuristics, artificial intelligence, analytics, and also multiple-criteria decision making. The number and the increasing size of the problems arising in real life require mathematical models and solution methods adequate to their complexity. There has also been increasing research interest in Big Data and related challenges. These challenges can be recognized in many fields and systems which have a significant impact on our way of living: design, management and control of industrial production of goods and services; transportation planning and traffic management in urban and regional areas; energy production and exploitation; natural resources and environment protection; homeland security and critical infrastructure protection; development of advanced information and communication technologies. The chapters in this book examine how to deal with new and emerging practical problems arising in these different fields through the presented methodologies and their applications. The chapter topics are applicable for researchers and practitioners working in these areas, but also for the operations research community. The contributions were presented during the international conference “Optimization and Decision Science” (ODS2017), held at Hilton Sorrento Palace Conference Center, Sorrento, Italy, September 4 – 7, 2017. ODS 2017, was organized by AIRO, Italian Operations Research Society, in cooperation with DIETI (Department of Electrical Engineering and Information Technology) of University “Federico II” of Naples.

Statistics Data Analysis And Decision Modeling

Author: James Robert Evans
Publisher:
ISBN: 9780130675538
Size: 60.55 MB
Format: PDF, ePub, Docs
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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.

Probability And Statistics Volume Ii

Author: Reinhard Viertl
Publisher: EOLSS Publications
ISBN: 1848260539
Size: 11.91 MB
Format: PDF, Kindle
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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.

Decision Sciences

Author: Raghu Nandan Sengupta
Publisher: CRC Press
ISBN: 1482282569
Size: 38.54 MB
Format: PDF, Docs
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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.

Data Analysis And Decision Making With Microsoft Excel Revised

Author: S. Albright
Publisher: Cengage Learning
ISBN: 0324662440
Size: 28.77 MB
Format: PDF, Docs
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The emphasis of the text is on data analysis, modeling, and spreadsheet use in statistics and management science. This text contains professional Excel software add-ins. The authors maintain the elements that have made this text a market leader in its first edition: clarity of writing, a teach-by-example approach, and complete Excel integration. This edition has been revised to be compatible with Excel 2007 and the corresponding add-ins for Excel 2007. If you have moved to Excel 2007, you should use this revised edition. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Random Graphs For Statistical Pattern Recognition

Author: David J. Marchette
Publisher: John Wiley & Sons
ISBN: 9780471722083
Size: 21.71 MB
Format: PDF, Docs
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A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the firstbook to address the topic of random graphs as it applies tostatistical pattern recognition. Both topics are of vital interestto researchers in various mathematical and statistical fields andhave never before been treated together in one book. The use ofdata random graphs in pattern recognition in clustering andclassification is discussed, and the applications for bothdisciplines are enhanced with new tools for the statistical patternrecognition community. New and interesting applications for randomgraph users are also introduced. This important addition to statistical literaturefeatures: Information that previously has been available only throughscattered journal articles Practical tools and techniques for a wide range of real-worldapplications New perspectives on the relationship between patternrecognition and computational geometry Numerous experimental problems to encourage practicalapplications With its comprehensive coverage of two timely fields, enhancedwith many references and real-world examples, Random Graphs forStatistical Pattern Recognition is a valuable resource forindustry professionals and students alike.