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Model Choice in Nonnested Families
by Basilio De Pereira Carlos Alberto PereiraThis book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
Model Discrimination for Nonlinear Regression Models (Statistics: A Series Of Textbooks And Monographs #101)
by Dale S. BorowiakBorowiak (math, U. of Akron) discusses model discrimination based upon incorrect selection probability, presents diagnostic statistics and formal hypothesis test procedures to assess a model's fit and stability, explains the use of computer computations such as the jackknife and bootstrap, and demon
Model Elements and Network Solutions of Heat, Mass and Momentum Transport Processes (Heat and Mass Transfer)
by George L. DankoThis work provides an enormous contribution to the broad effort of modeling heat, mass and momentum transport in multi-physics problems with the development of new solution approaches. It re-visits the time-honored technique of network application using flow network solutions for all transport process components for a coupled modeling task. The book further provides as formulation of the conservation laws for mass, energy and momentum, specifically for the branches and nodes of transport networks using the combination of the Eulerian and Lagrangean modeling methods. With the extension of Bernoulli’s original concept, a new solution is given for the flow field of viscous and compressible fluids as driven by the balance of mechanical energy, coupled to the thermodynamics of the transport system. Applicable to simple or large-scale tasks, the new model elements and methods are built on first principles. Throughout the work, the book provides original formulations, their mathematical derivations as well as applications in a numerical solution scheme.
Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
by Robert Colby Pethuru Raj Chelliah Amir Masoud Rahmani Gayathri Nagasubramanian Sunku RanganathComprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.
Model Order Reduction and Applications: Cetraro, Italy 2021 (Lecture Notes in Mathematics #2328)
by Michael Hinze Karsten Urban J. Nathan Kutz Olga MulaThis book addresses the state of the art of reduced order methods for modelling and computational reduction of complex parametrised systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in various fields.Consisting of four contributions presented at the CIME summer school, the book presents several points of view and techniques to solve demanding problems of increasing complexity. The focus is on theoretical investigation and applicative algorithm development for reduction in the complexity – the dimension, the degrees of freedom, the data – arising in these models.The book is addressed to graduate students, young researchers and people interested in the field. It is a good companion for graduate/doctoral classes.
Model Predictive Control: Approaches Based On The Extended State Space Model And Extended Non-minimal State Space Model
by Furong Gao Ridong Zhang Anke XueThis monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.
Model Predictive Control: Engineering Methods for Economists (Dynamic Modeling and Econometrics in Economics and Finance #31)
by Lars Grüne Gernot Tragler Josef Haunschmied Aris DaniilidisThe book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.
Model Predictive Vibration Control
by Boris Rohaľ-Ilkiv Gergely TakácsReal-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: · the implementation of computationally efficient algorithms · control strategies in simulation and experiment and · typical hardware requirements for piezoceramics actuated smart structures. The use of a simple laboratory model and inclusion of over 170 illustrations provides readers with clear and methodical explanations, making Model Predictive Vibration Control the ideal support material for graduates, researchers and industrial practitioners with an interest in efficient predictive control to be utilized in active vibration attenuation.
Model Reduction for Circuit Simulation
by Peter Benner E. Jan ter Maten Michael HinzeSimulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. Modern Model Order Reduction (MOR) techniques present a way out of this dilemma in providing surrogate models which keep the main characteristics of the device while requiring a significantly lower simulation time than the full model. With Model Reduction for Circuit Simulation we survey the state of the art in the challenging research field of MOR for ICs, and also address its future research directions. Special emphasis is taken on aspects stemming from miniturisations to the nano scale. Contributions cover complexity reduction using e.g., balanced truncation, Krylov-techniques or POD approaches. For semiconductor applications a focus is on generalising current techniques to differential-algebraic equations, on including design parameters, on preserving stability, and on including nonlinearity by means of piecewise linearisations along solution trajectories (TPWL) and interpolation techniques for nonlinear parts. Furthermore the influence of interconnects and power grids on the physical properties of the device is considered, and also top-down system design approaches in which detailed block descriptions are combined with behavioral models. Further topics consider MOR and the combination of approaches from optimisation and statistics, and the inclusion of PDE models with emphasis on MOR for the resulting partial differential algebraic systems. The methods which currently are being developed have also relevance in other application areas such as mechanical multibody systems, and systems arising in chemistry and to biology. The current number of books in the area of MOR for ICs is very limited, so that this volume helps to fill a gap in providing the state of the art material, and to stimulate further research in this area of MOR. Model Reduction for Circuit Simulation also reflects and documents the vivid interaction between three active research projects in this area, namely the EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium, The Netherlands and Germany), the EU-Marie Curie Action RTN-project COMSON (members in The Netherlands, Italy, Germany, and Romania), and the German federal project System reduction in nano-electronics (SyreNe).
Model Reduction of Complex Dynamical Systems (International Series of Numerical Mathematics #171)
by Peter Benner Michael Hinze Tatjana Stykel Ralf Zimmermann Tobias Breiten Heike FaßbenderThis contributed volume presents some of the latest research related to model order reduction of complex dynamical systems with a focus on time-dependent problems. Chapters are written by leading researchers and users of model order reduction techniques and are based on presentations given at the 2019 edition of the workshop series Model Reduction of Complex Dynamical Systems – MODRED, held at the University of Graz in Austria. The topics considered can be divided into five categories:system-theoretic methods, such as balanced truncation, Hankel norm approximation, and reduced-basis methods; data-driven methods, including Loewner matrix and pencil-based approaches, dynamic mode decomposition, and kernel-based methods;surrogate modeling for design and optimization, with special emphasis on control and data assimilation;model reduction methods in applications, such as control and network systems, computational electromagnetics, structural mechanics, and fluid dynamics; andmodel order reduction software packages and benchmarks.This volume will be an ideal resource for graduate students and researchers in all areas of model reduction, as well as those working in applied mathematics and theoretical informatics.
Model Selection and Error Estimation in a Nutshell (Modeling and Optimization in Science and Technologies #15)
by Luca OnetoHow can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.
Model Theoretic Algebra With Particular Emphasis on Fields, Rings, Modules
by Christian.U JensenThis volume highlights the links between model theory and algebra. The work contains a definitive account of algebraically compact modules, a topic of central importance for both module and model theory. Using concrete examples, particular emphasis is given to model theoretic concepts, such as axiomizability. Pure mathematicians, especially algebraists, ring theorists, logicians, model theorists and representation theorists, should find this an absorbing and stimulating book.
Model Theory and the Philosophy of Mathematical Practice: Formalization without Foundationalism
by John T. BaldwinMajor shifts in the field of model theory in the twentieth century have seen the development of new tools, methods, and motivations for mathematicians and philosophers. In this book, John T. Baldwin places the revolution in its historical context from the ancient Greeks to the last century, argues for local rather than global foundations for mathematics, and provides philosophical viewpoints on the importance of modern model theory for both understanding and undertaking mathematical practice. The volume also addresses the impact of model theory on contemporary algebraic geometry, number theory, combinatorics, and differential equations. This comprehensive and detailed book will interest logicians and mathematicians as well as those working on the history and philosophy of mathematics. Explains the philosophical significance of the transformation in model theory and its impact on traditional mathematics; The technical logic is grounded in historical and philosophical contexts, making the subject accessible to philosophers as well as mathematicians; Includes source materials from model theorists discussing their methods and motivation.
Model Theory in Algebra, Analysis and Arithmetic
by Anand Pillay Lou Dries Jochen Koenigsmann H. Dugald Macpherson Carlo Toffalori Alex J. WilkiePresenting recent developments and applications, the book focuses on four main topics in current model theory: 1) the model theory of valued fields; 2) undecidability in arithmetic; 3) NIP theories; and 4) the model theory of real and complex exponentiation. Young researchers in model theory will particularly benefit from the book, as will more senior researchers in other branches of mathematics.
Model Theory of Fields: Lecture Notes in Logic 5, Second Edition
by David Marker Margit Messmer Anand PillayThe model theory of fields is a fascinating subject stretching from Tarski's work on the decidability of the theories of the real and complex fields to Hrushovksi's recent proof of the Mordell-Lang conjecture for function fields. This volume provides an insightful introduction to this active area, concentrating on connections to stability theory.
Model Theory of Stochastic Processes: Lecture Notes in Logic 14
by Sergio Fajardo H. Jerome KeislerThis book presents new research in probability theory using ideas from mathematical logic. It is a general study of stochastic processes on adapted probability spaces, employing the concept of similarity of stochastic processes based on the notion of adapted distribution. The authors use ideas from model theory and methods from nonstandard analysis
Model Theory: Third Edition (Dover Books on Mathematics #58)
by H. Jerome Keisler C. C. ChangModel theory deals with a branch of mathematical logic showing connections between a formal language and its interpretations or models. This is the first and most successful textbook in logical model theory. Extensively updated and corrected in 1990 to accommodate developments in model theoretic methods -- including classification theory and nonstandard analysis -- the third edition added entirely new sections, exercises, and references. Each chapter introduces an individual method and discusses specific applications. Basic methods of constructing models include constants, elementary chains, Skolem functions, indiscernibles, ultraproducts, and special models. The final chapters present more advanced topics that feature a combination of several methods. This classic treatment covers most aspects of first-order model theory and many of its applications to algebra and set theory.
Model Validation and Uncertainty Quantification, Vol. 3: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Roland Platz Garrison Flynn Kyle Neal Scott OuelletteModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Uncertainty Quantification in Dynamics Fusion of Test and Analysis Model Form Uncertainty: Round Robin Challenge UQVI (Uncertainty Quantification in Vibration Isolation) Recursive Bayesian System Identification Virtual Sensing & Realtime Monitoring Surrogate Modeling and Reduced Order Models
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics 2018 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Robert BarthorpeModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics, 2018, the third volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:Uncertainty Quantification in Material ModelsUncertainty Propagation in Structural DynamicsPractical Applications of MVUQAdvances in Model Validation & Uncertainty Quantification: Model UpdatingModel Validation & Uncertainty Quantification: Industrial ApplicationsControlling UncertaintyUncertainty in Early Stage DesignModeling of Musical InstrumentsOverview of Model Validation and Uncertainty
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Zhu MaoModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the third volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:Inverse Problems and Uncertainty QuantificationControlling UncertaintyValidation of Models for Operating EnvironmentsModel Validation & Uncertainty Quantification: Decision MakingUncertainty Quantification in Structural DynamicsUncertainty in Early Stage DesignComputational and Uncertainty Quantification Tools
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Roland Platz Garrison Flynn Kyle Neal Scott OuelletteModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:Introduction of Uncertainty QuantificationUncertainty Quantification in DynamicsModel Form Uncertainty and Selection incl. Round Robin ChallengeSensor and Information FusionVirtual Sensing, Certification, and Real-Time MonitoringSurrogate Modeling
Model and Data Engineering: 13th International Conference, MEDI 2024, Naples, Italy, November 18–20, 2024, Proceedings (Lecture Notes in Computer Science #15590)
by Ladjel Bellatreche Elio Masciari Carlos Ordonez Giancarlo SperlìThis book constitutes the refereed proceedings of the 13th International Conference onModel and Data Engineering, MEDI 2024, held in Naples, Italy, during November 18–20, 2024. The 7 full papers and 11 short papers were carefully peer reviewed and selected from 45 submissions.They were organized in topical sections as follows: AI-Enabled Systems; Security and Privacy; Query Processing; Prediction; Conceptual Issues; and Applications.
Model and Data Engineering: 8th International Conference, MEDI 2018, Marrakesh, Morocco, October 24–26, 2018, Proceedings (Lecture Notes in Computer Science #11163)
by Ladjel Bellatreche Carlos Ordonez El Hassan Abdelwahed Mattéo Golfarelli Dominique MéryThis book constitutes the refereed proceedings of the 8h International Conference on Model and Data Engineering, MEDI 2018, held in Marrakesh, Morocco, in October 2018.The 23 full papers and 4 short papers presented together with 2 invited talks were carefully reviewed and selected from 86 submissions. The papers covered the recent and relevant topics in the areas of databases; ontology and model-driven engineering; data fusion, classsification and learning; communication and information technologies; safety and security; algorithms and text processing; and specification, verification and validation.
Model and Mathematics: From the 19th to the 21st Century (Trends in the History of Science)
by Michael Friedman Karin KrauthausenThis open access book collects the historical and medial perspectives of a systematic and epistemological analysis of the complicated, multifaceted relationship between model and mathematics, ranging from, for example, the physical mathematical models of the 19th century to the simulation and digital modelling of the 21st century. The aim of this anthology is to showcase the status of the mathematical model between abstraction and realization, presentation and representation, what is modeled and what models.This book is open access under a CC BY 4.0 license.
Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications (Chapman & Hall/CRC Biostatistics Series)
by Ying Yuan J. Jack Lee Ruitao LinBayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol—making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges, issues, and solutions arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustrations of using software to design trials Develops a companion website (www.trialdesign.org) to provide freely available, easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the design, conduct, and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!