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Bayesian Approach to Inverse Problems

problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three;

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Bayesian Missing Data Problems

author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional;

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Bayesian Missing Data Problems

author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional;

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Inverse Problems

Inverse problems arise in practical applications whenever one needs to deduce unknowns from observables. This monograph is a valuable;

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Numerical Methods for Inverse Problems

regularization; finite differences; and orthogonal decomposition; as well as those based on Bayesian inference, artificial neural networks, and quantum;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

examples that explain techniques which are useful to address general problems arising in uncertainty quantification; Bayesian and non-Bayesian;

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Bayesian Field Theory

thousand tosses; a subsequent experiment would then be used to refine this prediction. The Bayesian approach, in other words, permits the use;

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Bayesian Inverse Problems

. Understand Bayesian methods including filtering techniques, Bayesian networks, etc., and their use in engineering systems analysis. Learn to;

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Inverse Heat Transfer

the Bayesian framework of statistics for the solution of inverse problems. By modernizing the classic work of the late Professor M. Necati;

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Variational Regularization for Systems of Inverse Problems

Huber discusses a multi-parameter Tikhonov approach for systems of inverse problems in order to take advantage of their specific structure. Such;

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Large-Scale Inverse Problems and Quantification of Uncertainty

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical;

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Inverse Heat Transfer

of inverse heat transfer protocol, as a parameter estimation approach and as a function estimation approach. These techniques are then applied to the;

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Non-Extensive Entropy Econometrics for Low Frequency Series

approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium;

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Non-Extensive Entropy Econometrics for Low Frequency Series

entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based;

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Inverse Problems

the author's approach, readers will understand the relevant theory and methodology needed to pursue more complex applications. Inverse;

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Applications of Elliptic Carleman Inequalities to Cauchy and Inverse Problems

This book presents a unified approach to studying the stability of both elliptic Cauchy problems and selected inverse problems. Based on;

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Inverse Boundary Spectral Problems

. The authors approach inverse problems in a coordinate invariant way, that is, by applying ideas drawn from differential geometry. To solve;

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Inverse Boundary Spectral Problems

. The authors approach inverse problems in a coordinate invariant way, that is, by applying ideas drawn from differential geometry. To solve;

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Bayesian Inference for Probabilistic Risk Assessment

can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual;

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Direct and Inverse Sturm-Liouville Problems

This book provides an introduction to the most recent developments in the theory and practice of direct and inverse Sturm-Liouville;

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Maximum Entropy and Bayesian Methods

Mexico, USA, from July 31 to August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific;

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Inverse Problems in the Mathematical Sciences

problems receive scant attention in the university curriculum. This book aims to remedy this state of affairs by supplying an accessible;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Bayesian Biostatistics

This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian;

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Bayesian Biostatistics

This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian;

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Think Bayes

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with;

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