This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse a
This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection desig
This monograph presents new model-based design methods for trajectory planning, feedback stabilization, state estimation, and tracking control of distributed-parameter systems governed by partial diff
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a
Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear algebra required for statistics followed by the basic aspects of the theory of linear estimation and hypothesis
The present book analyses critically the tripartite mimicry model (consisting of the mimic, model and receiver species) and develops semiotic tools for comparative analysis. It is proposed that mimicr
This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and t
Pro Spring Boot is your authoritative hands-on practical guide for increasing your Spring Framework-based enterprise Java and cloud application productivity while decreasing development time using the
This book systematically presents energy-efficient robust fusion estimation methods to achieve thorough and comprehensive results in the context of network-based fusion estimation. It summarizes recen
This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definit
This book provides a clear and concise introduction to radar signal processing for nonspecialists - those who want to perform image enhancement, parameter estimation, pattern recognition, and other co
Control of Linear Parameter Varying Systems compiles state-of-the-art contributions on novel analytical and computational methods for addressing system identification, model reduction, performance ana
Whole new areas of immunological research are emerging from the analysis of experimental data, going beyond statistics and parameter estimation into what an applied mathematician would recognise as mo
This collected work reports on the state of the art of hydrological model simulation, as well as the methods for satellite-based rainfall estimation. Mainly addressed to scientists and researchers, th
The subject of the book is to present the modeling, parameter estimation and other aspects of the identification of nonlinear dynamic systems. The treatment is restricted to the input-output modeling
The book discusses the estimation theory for the wide class of inhomogeneous Poisson processes. The consistency, limit distributions and the convergence of moments of parameter estimators are establis
Mechanics and Model-Based Control of Advanced Engineering Systems collects 32 contributions presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machi
The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it considers the full model, submodel, penal
This book provides a unified IQ imbalance model and systematically reviews the existing estimation and compensation schemes. It covers the different assumptions and approaches that lead to many models
Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehic
Our book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo e
In 1982, Springer published the English translation of the Russian book Estimation of Dependencies Based on Empirical Data which became the foundation of the statistical theory of learning and general
This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve
This book explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations, in partic
This book describes model-based development of adaptive embedded systems, which enable improved functionality using the same resources. The techniques presented facilitate design from a higher level o
The book draws on current research on model risk and parameter sensitivity of securitisation ratings. It provides practical ideas and tools that can facilitate a more informed usage of securitisation
This textbook provides a comprehensive and current understanding of signal detection and estimation, including problems and solutions for each chapter. Signal detection plays an important role in fiel
Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insenistive with respect to random parameter v
Anyone involved in science education will find that this text can enhance their pedagogical practice. It describes new, model-based teaching methods that integrate social and cognitive perspectives fo
This volume is the first book-length treatment of model-based geostatistics. The text is expository, emphasizing statistical methods and applications rather than the underlying mathematical theory. An
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and u
This volume is the first to present a unified perspective on the control of underactuated mechanical systems. Based on real-time implementation of parameter identification, this book provides a variet
This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function depende
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As su
This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily
This monograph presents a unified approach to model-based processing for underwater acoustic arrays. The use of physical models in passive array processing is not a new idea, but it has been used on a
This book presents a discipline-based teaching and learning center (TLC) as an effective model for providing professional development to faculty and graduate students. It provides a theoretical and pr
This book presents an agile and model-driven approach to manage scientific workflows. The approach is based on the Extreme Model Driven Design (XMDD) paradigm and aims at simplifying and automating th
