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Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the;

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Surrogates

/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning, local GP approximation, modeling of simulation experiments;

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Surrogates

learning and (blackbox/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning, local GP approximation, modeling;

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Non-Gaussian Random Vibration Fatigue Analysis and Accelerated Test

include statistical analysis method of non-Gaussian random vibration, modeling and simulation of non-Gaussian/non-stationary random vibration;

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Towards Intelligent Modeling: Statistical Approximation Theory

limit fails. The results of this book not only cover the classical and statistical approximation theory, but also are applied in the fuzzy logic;

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Statistical Modeling and Computation

authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models;

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Asymptotic Statistics

This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Statistical Methods Of Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Statistical Portfolio Estimation

framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many;

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Statistical Portfolio Estimation

framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many;

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Dynamics of Statistical Experiments

the representation of a stationary Gaussian statistical experiment with the Markov property, as a stochastic difference equation solution;

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Multiscale Signal Analysis and Modeling

Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This;

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Extreme Value Theory-based Methods for Visual Recognition

several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability;

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Molecular Modeling of Inclusion Complexes

Gaussian 03 software. The UV-Vis spectroscopy results were found to be comparable with the quantum mechanics calculations performed using the;

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Modeling Fluctuations in Scattered Waves

applications of these models. The first two chapters introduce statistical tools and the properties of Gaussian noise, including results on phase;

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Time Series Modeling of Neuroscience Data

data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods;

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VaR Methodology for Non-Gaussian Finance

approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Levy;

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Theory and Applications of Gaussian Quadrature Methods

of this work is to provide an introduction to the theory and practice of Gaussian quadrature. We study the approximation theory of trigonometric;

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Introduction to Time Series Modeling with Applications in R

and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space;

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Orthogonal Functions

functions, moment theory, and integral transforms. Describes links between continued fractions. Pade approximation, special functions, and Gaussian;

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Generalized Linear Mixed Models

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the;

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Markov Processes, Gaussian Processes, and Local Times

properties of Markov processes by using well-established techniques in Gaussian process theory. This original, readable book will appeal to both;

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Current-Mode Analog Nonlinear Function Synthesizer Structures

-order approximation functions. A variety of analog function synthesizer structures are discussed, based on accurate approximation functions;

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Functional Gaussian Approximation for Dependent Structures

Functional Gaussian Approximation for Dependent Structures develops and analyses mathematical models for phenomena that evolve in time and;

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