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Optimization Techniques In Statistics

Author: Jagdish S. Rustagi
Publisher: Elsevier
ISBN: 1483295710
Size: 26.53 MB
Format: PDF, Kindle
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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: 46.46 MB
Format: PDF, Kindle
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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: Prentice Hall
ISBN: 9780131886094
Size: 68.41 MB
Format: PDF, 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.

Decision Sciences

Author: Raghu Nandan Sengupta
Publisher: CRC Press
ISBN: 1482282569
Size: 57.47 MB
Format: PDF, ePub
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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.

Strengthening Data Science Methods For Department Of Defense Personnel And Readiness Missions

Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309450780
Size: 38.61 MB
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The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members’ career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€"exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€"these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R.

Data Analysis And Decision Making

Author: S. Albright
Publisher: Cengage Learning
ISBN: 0538476125
Size: 11.12 MB
Format: PDF, ePub, Mobi
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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.

Random Graphs For Statistical Pattern Recognition

Author: David J. Marchette
Publisher: John Wiley & Sons
ISBN: 9780471722083
Size: 19.61 MB
Format: PDF, ePub
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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.

Modes Of Parametric Statistical Inference

Author: Seymour Geisser
Publisher: John Wiley & Sons
ISBN: 0471743127
Size: 39.98 MB
Format: PDF
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A fascinating investigation into the foundations of statisticalinference This publication examines the distinct philosophical foundations ofdifferent statistical modes of parametric inference. Unlike manyother texts that focus on methodology and applications, this bookfocuses on a rather unique combination of theoretical andfoundational aspects that underlie the field of statisticalinference. Readers gain a deeper understanding of the evolution andunderlying logic of each mode as well as each mode's strengths andweaknesses. The book begins with fascinating highlights from the history ofstatistical inference. Readers are given historical examples ofstatistical reasoning used to address practical problems that arosethroughout the centuries. Next, the book goes on to scrutinize fourmajor modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples andcounterexamples of situations and datasets where the modes yieldboth similar and dissimilar results, including a violation of thelikelihood principle in which Bayesian and likelihood methodsdiffer from frequentist methods. Each example is followed by adetailed discussion of why the results may have varied from onemode to another, helping the reader to gain a greater understandingof each mode and how it works. Moreover, the author providesconsiderable mathematical detail on certain points to highlight keyaspects of theoretical development. The author's writing style and use of examples make the text clearand engaging. This book is fundamental reading for graduate-levelstudents in statistics as well as anyone with an interest in thefoundations of statistics and the principles underlying statisticalinference, including students in mathematics and the philosophy ofscience. Readers with a background in theoretical statistics willfind the text both accessible and absorbing.

Applied Data Analysis And Modeling For Energy Engineers And Scientists

Author: T. Agami Reddy
Publisher: Springer Science & Business Media
ISBN: 9781441996138
Size: 59.31 MB
Format: PDF, ePub, Mobi
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Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.