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Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites

is augmented numerical efficiency, which is more accurate for given computational costs than existing methods and methodologies. It first presents;

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Partially Linear Models

efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and;

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Introduction to Linear Goal Programming

Provides a concise and lucid overview of the linear goal programming model, a computationally efficient algorithm for solution, duality and;

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Numerical Methods for Linear Control Systems

implementations of numerically-viable algorithms based on well-established, efficient and stable modern numerical linear techniques for mathematical;

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Personalized Predictive Modeling in Type 1 Diabetes

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to;

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Nonlinear Behavior of Ceramic-Matrix Composites

Nonlinear Damage Behavior of Ceramic Matrix Composites help readers [researchers, material scientists and design engineers] gain greater;

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Design and Analysis of Reinforced Fiber Composites

. Linear and nonlinear structural behavior (from linear buckling to nonlinear yelding and fracture) are discussed as well as different materials;

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Foundations of Predictive Analytics

, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis;

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Foundations of Predictive Analytics

, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis;

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Nonlinear Time Series Analysis

nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and;

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Recent Advances in Estimating Nonlinear Models

number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and;

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Time Series Modeling of Neuroscience Data

approach, in which dynamic models of all kinds, such as linearnonlinear differential equation models and time series models, are used for;

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Short-Memory Linear Processes and Econometric Applications

(CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their;

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Iterative Methods for Linear and Nonlinear Equations

This book is devoted to giving a modern view of iterative methods for solving linear and nonlinear equations, which are the basis for many;

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Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning;

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Structure and Rheology of Molten Polymers

of linear behavior. Tube models for both linear and branched polymers are presented. The final two chapters deal with nonlinear rheological behavior;

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Comparison of the 2D and 3D Analyses Methods for Cfrds

between detailed analysis of 2D models and linear elastic 2D models were covered to get practical and industrial solutions for the guiding methods;

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Dynamic Models Of Infectious Diseases

technology has paved way for the development of new tools and methodologies to fight against these diseases. In particular, intelligent technology;

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Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the;

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Spectral Theory and Nonlinear Functional Analysis

to linear algebra, linear and nonlinear functional analysis, and topology and opens the door for further advances.;

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Spectral Theory and Nonlinear Functional Analysis

to linear algebra, linear and nonlinear functional analysis, and topology and opens the door for further advances.;

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Mathematical Models of Non-linear Phenomena, Processes & Systems

mathematical treatment of non-linear problems, including the methodologies, algorithms and properties of analytical and numerical solutions;

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Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming

Interest in constrained optimization originated with the simple linear pro gramming model since it was practical and perhaps the only;

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Predictive Control for Linear and Hybrid Systems

feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid;

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Predictive Control for Linear and Hybrid Systems

feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.;

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