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Modeling Social Processes of Aggregation
by Lawrence HazelriggThis book demonstrates, via formal statements and empirical illustrations, that nonlinearities in social processes can be modeled systematically to create solutions with practical applications in the institutional forms of paid employment, schooling, and familial relations including marital and kinship ties and the rearing of children. It shows how social processes can be modeled accurately through analyzing time series data—specifically, a temporal sequence of process outcomes that is dense enough in observation time to support appropriate techniques of modeling the outcome sequence. The book illustrates techniques using minimal mathematical formalism which is explained also in careful narrative descriptions of the model logic.
Modeling Software with Finite State Machines: A Practical Approach
by Thomas Wagner Ferdinand Wagner Ruedi Schmuki Peter WolstenholmeModeling Software with Finite State Machines: A Practical Approach explains how to apply finite state machines to software development. It provides a critical analysis of using finite state machines as a foundation for executable specifications to reduce software development effort and improve quality. It discusses the design of a state machine and of a system of state machines. It also presents a detailed analysis of development issues relating to behavior modeling with design examples and design rules for using finite state machines. This text demonstrates the implementation of these concepts using StateWORKS software and introduces the basic components of this software.
Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models
by Ferreira, Marco A. R.Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.Key topics: Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models. Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection. Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects. Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations. Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression. Multiscale spatio-temporal assimilation of computer model output and monitoring station data. Dynamic multiscale heteroscedastic multivariate spatio-temporal models. The M-open multiple optima paradox and some of its practical implications for multiscale modeling. Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes. The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.
Modeling Students' Mathematical Modeling Competencies
by Andrew Hurford Christopher R. Haines Peter L. Galbraith Richard LeshAs we enter the 21st century, there is an urgent need for new approaches to mathematics education emphasizing its relevance in young learners' futures. Modeling Students' Mathematical Modeling Competencies explores the vital trend toward using real-world problems as a basis for teaching mathematics skills, competencies, and applications. Blending theoretical constructs and practical considerations, the book presents papers from the latest conference of the ICTMA, beginning with the basics (Why are models necessary? Where can we find them?) and moving through intricate concepts of how students perceive math, how instructors teach--and how both can become better learners. Dispatches as varied as classroom case studies, analyses of math in engineering work, and an in-depth review of modeling-based curricula in the Netherlands illustrate modeling activities on the job, methods of overcoming math resistance, and the movement toward replicable models and lifelong engagement. A sampling of topics covered: How students recognize the usefulness of mathematicsCreating the modeling-oriented classroomAssessing and evaluating students' modeling capabilitiesThe relationship between modeling and problem-solvingInstructor methods for developing their own models of modelingNew technologies for modeling in the classroomModeling Students' Mathematical Modeling Competencies offers welcome clarity and focus to the international research and professional community in mathematics, science, and engineering education, as well as those involved in the sciences of teaching and learning these subjects.
Modeling Survival Data Using Frailty Models: Second Edition (Industrial and Applied Mathematics)
by David D. HanagalThis book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.
Modeling Tools for Environmental Engineers and Scientists
by Nirmala KhandanModeling Tools for Environmental Engineers and Scientists enables environmental professionals, faculty, and students with minimal computer programming skills to develop computer-based mathematical models for natural and engineered environmental systems. The author illustrates how commercially available syntax-free authoring software can be adapted
Modeling Visual Aesthetics, Emotion, and Artistic Style
by James Z. Wang Reginald B. Adams Jr.Modeling Visual Aesthetics, Emotion, and Artistic Style offers a comprehensive exploration of the increasingly significant topic of the complex interplay between human perception and digital technology. It embodies the cumulative knowledge and efforts of a wide array of active researchers and practitioners from diverse fields including computer vision, affective computing, robotics, psychology, data mining, machine learning, art history, and movement analysis. This volume seeks to address the profound and challenging research questions related to the computational modeling and analysis of visual aesthetics, emotions, and artistic style, vital components of the human experience that are increasingly relevant in our digitally connected world. The book's vast scope encompasses a broad range of topics. The initial chapters lay a strong foundation with background knowledge on emotion models and machine learning, which then transitions into exploring social visual perception in humans and its technological applications. Readers will uncover the psychological and neurological foundations of social and emotional perception from faces and bodies. Subsequent sections broaden this understanding to include technology's role in detecting discrete and subtle emotional expressions, examining facial neutrality, and including research contexts that involve children as well as adults. Furthermore, the book illuminates the dynamic intersection of art and technology, the language of photography, the relationship between breath-driven robotic performances and human dance, and the application of machine learning in analyzing artistic styles. This book sets itself apart with its unique multidisciplinary approach, encouraging collaboration across related domains. Packed with comprehensive tutorials, theoretical reviews, novel methodologies, empirical investigations, and comparative analyses, the book offers a rich combination of knowledge and methodologies. The book's focus on cutting-edge research not only presents the latest developments in the field but also illuminates potential paths that can lead to significant advancements in computer and robotic applications.
Modeling Waves with Numerical Calculations Using Python (Synthesis Lectures on Wave Phenomena in the Physical Sciences)
by Rhett AllainNumerical calculations (what many call computational physics) is a core tool in modern physics. With numerical methods it’s possible to solve problems that would otherwise be impossible. Most physics students and educators have at least some exposure to the wave equation. It shows up in many different contexts—light, quantum mechanics, and even a simple wave on a string. However, it can be difficult to come up with non-trivial solutions to the wave equation. This text goes through the techniques to create a numerical model of the wave equation starting from the very basics and using free and open source tools such as Python and Web VPython.
Modeling and Analysis of Compositional Data (Statistics in Practice)
by Vera Pawlowsky-Glahn Raimon Tolosana-Delgado Juan José EgozcueModeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn’s earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.
Modeling and Analysis of Dynamic Systems
by Ramin S. Esfandiari Bei LuModeling and Analysis of Dynamic Systems, Third Edition introduces MATLAB®, Simulink®, and Simscape™ and then utilizes them to perform symbolic, graphical, numerical, and simulation tasks. Written for senior level courses/modules, the textbook meticulously covers techniques for modeling a variety of engineering systems, methods of response analysis, and introductions to mechanical vibration, and to basic control systems. These features combine to provide students with a thorough knowledge of the mathematical modeling and analysis of dynamic systems. The Third Edition now includes Case Studies, expanded coverage of system identification, and updates to the computational tools included.
Modeling and Analysis of Linear Hyperbolic Systems of Balance Laws
by Krzysztof BarteckiThismonograph focuses on the mathematical modeling of distributed parameter systemsin which mass/energy transport or wave propagation phenomena occur and whichare described by partial differential equations of hyperbolic type. The case oflinear (or linearized) 2 x 2 hyperbolic systems of balance laws isconsidered, i. e. , systems described by two coupled linear partial differentialequations with two variables representing physical quantities, depending onboth time and one-dimensional spatial variable. Basedon practical examples of a double-pipe heat exchanger and a transportationpipeline, two typical configurations of boundary input signals are analyzed: collocated, wherein both signals affect the system at the samespatial point, and anti-collocated, inwhich the input signals are applied to the two different end points of thesystem. Theresults of this book emerge from the practical experience of the author gainedduring his studies conducted in the experimental installation of a heatexchange center as well as from his research experience in the field of mathematicaland computer modeling of dynamic systems. The book presents valuable resultsconcerning their state-space, transfer function and time-domain representations,which can be useful both for the open-loop analysis as well as for theclosed-loop design. Thebook is primarily intended to help professionals as well as undergraduate andpostgraduate students involved in modeling and automatic control of dynamicsystems.
Modeling and Analysis of Stochastic Systems (Chapman & Hall/CRC Texts in Statistical Science)
by Vidyadhar G. KulkarniBuilding on the author’s more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. The third edition has been updated with several new applications, including the Google search algorithm in discrete time Markov chains, several examples from health care and finance in continuous time Markov chains, and square root staffing rule in Queuing models. More than 50 new exercises have been added to enhance its use as a course text or for self-study. The sequence of chapters and exercises has been maintained between editions, to enable those now teaching from the second edition to use the third edition. Rather than offer special tricks that work in specific problems, this book provides thorough coverage of general tools that enable the solution and analysis of stochastic models. After mastering the material in the text, readers will be well-equipped to build and analyze useful stochastic models for real-life situations.
Modeling and Analytical Methods in Tribology
by Ilya I. Kudish Michael Judah CovitchImproving our understanding of friction, lubrication, and fatigue, Modeling and Analytical Methods in Tribology presents a fresh approach to tribology that links advances in applied mathematics with fundamental problems in tribology related to contact elasticity, fracture mechanics, and fluid film lubrication. The authors incorporate the classical
Modeling and Control for Micro/Nano Devices and Systems (Automation and Control Engineering #54)
by Author Mingjun Zhang Ning XiMicro/nano-scale engineering—especially the design and implementation of ultra-fast and ultra-scale energy devices, sensors, and cellular and molecular systems—remains a daunting challenge. Modeling and control has played an essential role in many technological breakthroughs throughout the course of history. Therefore, the need for a practical guide to modeling and control for micro/nano-scale devices and systems has emerged. The first edited volume to address this rapidly growing field, Modeling and Control for Micro/Nano Devices and Systems gives control engineers, lab managers, high-tech researchers, and graduate students easy access to the expert contributors’ cutting-edge knowledge of micro/nanotechnology, energy, and bio-systems. The editors offer an integrated view from theory to practice, covering diverse topics ranging from micro/nano-scale sensors to energy devices and control of biology systems in cellular and molecular levels. The book also features numerous case studies for modeling of micro/nano devices and systems, and explains how the models can be used for control and optimization purposes. Readers benefit from learning the latest modeling techniques for micro/nano-scale devices and systems, and then applying those techniques to their own research and development efforts.
Modeling and Control in Vibrational and Structural Dynamics: A Differential Geometric Approach (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science)
by Peng-Fei YaoModeling and Control in Vibrational and Structural Dynamics: A Differential Geometric Approach describes the control behavior of mechanical objects, such as wave equations, plates, and shells. It shows how the differential geometric approach is used when the coefficients of partial differential equations (PDEs) are variable in space (waves/plates),
Modeling and Differential Equations in Biology (Lecture Notes In Pure And Applied Mathematics Ser. #58)
by T. A. BurtonFirst published in 1980. CRC Press is an imprint of Taylor & Francis.
Modeling and Interpreting Interactive Hypotheses in Regression Analysis
by Cindy D. Kam Robert J. Franzese Jr.Social scientists study complex phenomena about which they often propose intricate hypotheses tested with linear-interactive or multiplicative terms. While interaction terms are hardly new to social science research, researchers have yet to develop a common methodology for using and interpreting them. Modeling and Interpreting Interactive Hypotheses in Regression Analysisprovides step-by-step guidance on how to connect substantive theories to statistical models and how to interpret and present the results. "Kam and Franzese is a must-have for all empirical social scientists interested in teasing out the complexities of their data. " ---Janet M. Box-Steffensmeier, Ohio State University "Kam and Franzese have written what will become the definitive source on dealing with interaction terms and testing interactive hypotheses. It will serve as the standard reference for political scientists and will be one of those books that everyone will turn to when helping our students or doing our work. But more than that, this book is the best text I have seen for getting students to really think about the importance of careful specification and testing of their hypotheses. " ---David A. M. Peterson, Texas A&M University "Kam and Franzese have given scholars and teachers of regression models something they've needed for years: a clear, concise guide to understanding multiplicative interactions. Motivated by real substantive examples and packed with valuable examples and graphs, their book belongs on the shelf of every working social scientist. " ---Christopher Zorn, University of South Carolina "Kam and Franzese make it easy to model what good researchers have known for a long time: many important and interesting causal effects depend on the presence of other conditions. Their book shows how to explore interactive hypotheses in your own research and how to present your results. The book is straightforward yet technically sophisticated. There are no more excuses for misunderstanding, misrepresenting, or simply missing out on interaction effects!" ---Andrew Gould, University of Notre Dame Cindy D. Kam is Assistant Professor, Department of Political Science, University of California, Davis. Robert J. Franzese Jr. is Associate Professor, Department of Political Science, University of Michigan, and Research Associate Professor, Center for Political Studies, Institute for Social Research, University of Michigan. For datasets, syntax, and worksheets to help readers work through the examples covered in the book, visit: www. press. umich. edu/KamFranzese/Interactions. html
Modeling and Inverse Problems in the Presence of Uncertainty (Chapman & Hall/CRC Monographs and Research Notes in Mathematics)
by H. T. Banks Shuhua Hu W. Clayton ThompsonModeling and Inverse Problems in the Presence of Uncertainty collects recent research-including the authors' own substantial projects-on uncertainty propagation and quantification. It covers two sources of uncertainty: where uncertainty is present primarily due to measurement errors and where uncertainty is present due to the modeling formulation i
Modeling and Optimization in Space Engineering
by Giorgio Fasano János D. PintérThis volume presents a selection of advanced case studies that address a substantial range of issues and challenges arising in space engineering. The contributing authors are well-recognized researchers and practitioners in space engineering and in applied optimization. The key mathematical modeling and numerical solution aspects of each application case study are presented in sufficient detail. Classic and more recent space engineering problems - including cargo accommodation and object placement, flight control of satellites, integrated design and trajectory optimization, interplanetary transfers with deep space manoeuvres, low energy transfers, magnetic cleanliness modeling, propulsion system design, sensor system placement, systems engineering, space traffic logistics, and trajectory optimization - are discussed. Novel points of view related to computational global optimization and optimal control, and to multidisciplinary design optimization are also given proper emphasis. A particular attention is paid also to scenarios expected in the context of future interplanetary explorations. Modeling and Optimization in Space Engineering will benefit researchers and practitioners working on space engineering applications. Academics, graduate and post-graduate students in the fields of aerospace and other engineering, applied mathematics, operations research and optimal control will also find the book useful, since it discusses a range of advanced model development and solution techniques and tools in the context of real-world applications and new challenges.
Modeling and Optimization in Space Engineering: State of the Art and New Challenges (Springer Optimization and Its Applications #144)
by Giorgio Fasano János D. PintérThis book presents advanced case studies that address a range of important issues arising in space engineering. An overview of challenging operational scenarios is presented, with an in-depth exposition of related mathematical modeling, algorithmic and numerical solution aspects. The model development and optimization approaches discussed in the book can be extended also towards other application areas. The topics discussed illustrate current research trends and challenges in space engineering as summarized by the following list: • Next Generation Gravity Missions • Continuous-Thrust Trajectories by Evolutionary Neurocontrol • Nonparametric Importance Sampling for Launcher Stage Fallout • Dynamic System Control Dispatch • Optimal Launch Date of Interplanetary Missions • Optimal Topological Design • Evidence-Based Robust Optimization • Interplanetary Trajectory Design by Machine Learning • Real-Time Optimal Control • Optimal Finite Thrust Orbital Transfers • Planning and Scheduling of Multiple Satellite Missions • Trajectory Performance Analysis • Ascent Trajectory and Guidance Optimization • Small Satellite Attitude Determination and Control • Optimized Packings in Space Engineering • Time-Optimal Transfers of All-Electric GEO Satellites Researchers working on space engineering applications will find this work a valuable, practical source of information. Academics, graduate and post-graduate students working in aerospace, engineering, applied mathematics, operations research, and optimal control will find useful information regarding model development and solution techniques, in conjunction with real-world applications.
Modeling and Optimization: Theory and Applications
by Tamás Terlaky Boris DefournyThis volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Modeling and Optimization: Theory and Applications
by Tamás Terlaky Luis F. ZuluagaThis volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on July 30-August 1, 2012. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of optimization techniques in finance, logistics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Modeling and Optimization: Theory and Applications
by Tamás Terlaky Martin TakáčThis volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 17-19, 2016. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Modeling and Reasoning with Bayesian Networks
by Adnan DarwicheThis 2009 book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
Modeling and Simulating Complex Business Perceptions: Using Graphical Models and Fuzzy Cognitive Maps (Fuzzy Management Methods)
by Zoumpolia DikopoulouFuzzy cognitive maps (FCMs) have gained popularity in the scientific community due to their capabilities in modeling and decision making for complex problems.This book presents a novel algorithm called glassoFCM to enable automatic learning of FCM models from data. Specifically, glassoFCM is a combination of two methods, glasso (a technique originated from machine learning) for data modeling and FCM simulation for decision making. The book outlines that glassoFCM elaborates simple, accurate, and more stable models that are easy to interpret and offer meaningful decisions. The research results presented are based on an investigation related to a real-world business intelligence problem to evaluate characteristics that influence employee work readiness.Finally, this book provides readers with a step-by-step guide of the 'fcm' package to execute and visualize their policies and decisions through the FCM simulation process.