[PDF] Top 20 Sparse deterministic approximation of Bayesian inverse problems
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Sparse deterministic approximation of Bayesian inverse problems
... whether sparse approximation techniques can be used to approximate the posterior density and conditional expectations given the ...elliptic inverse problem. Elliptic problems with random ... See full document
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QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
... optimization problems arising from high-dimensional statistical es- timation however, standard optimization methods typically suffer sub-linear rates of conver- gence (Agarwal et ... See full document
37
Ensemble based methods for geometric inverse problems
... a Bayesian approach, the inverse eikonal ...the inverse problem associated with it has seen a lack of ...a Bayesian formulation of the ...how inverse HJ equations act under the ... See full document
194
Efficient MCMC and posterior consistency for Bayesian inverse problems
... better approximation to the underlying continuum model or by increasing the number of samples used in the ...between approximation and Monte-Carlo error which has quan- titatively been investigated in ... See full document
284
Bayesian approach with prior models which enforce sparsity in signal and image processing
... the Bayesian inference approach for inverse problems in signal and image processing, where we want to infer on sparse signals or ...the Bayesian computations (optimization for the joint ... See full document
19
A Bayesian level set method for geometric inverse problems
... Geometric inverse problems, in which the interfaces between different domains are the primary un- known quantities, are ubiquitous in applications including medical imaging problems such as EIT [9] ... See full document
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NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction
... practical problems, such as medical image reconstruction, the functions or images that one wants to recover are often subject to sharp jumps or ...the deterministic setting; however, it has been proven in ... See full document
18
Sparse Regularization for Inverse Problems Governed by Evolution Equations.
... In this section, we develop and analyze a new approach for estimating the initial condition of the abstract Cauchy problem (2.1.1) from time-series data. This method is similar to the LLS/RLLS methods presented in the ... See full document
126
Approximation Hardness for A Class of Sparse Optimization Problems
... three factors: (i) properties of the regularization penalty λ · κ; (ii) data size n; and (iii) dimension or number of variables d. This result illustrates a fundamental gap that can not be closed by any polynomial-time ... See full document
27
Adding Constraints to Bayesian Inverse Problems
... specific Bayesian filter, and most of them are based on a linearized form of the constraints, which is limiting when constraint functions are complicated and highly ...a deterministic way may neglect the ... See full document
8
Analysis and computation for Bayesian inverse problems
... An overview of the area of statistical, and in particular, Bayesian approaches to inverse problems is provided in the text [75], with a strong focus on cases where Lebesgue densities exist. This is ... See full document
192
Approximation of Bayesian inverse problems for PDEs
... the approximation of the forward problem (a form of consistency), along with well posed- ness of the inverse problem, implies convergence of the posterior ...most inverse problem ...such ... See full document
25
Atmospheric inverse modeling via sparse reconstruction
... Inversions with non-Gaussian priors, like the Laplacian in Eqs. (6) and (7), rarely have an analytical solution simpli- fying the calculation of the posterior distribution. The pos- terior distribution can also be ... See full document
19
Randomized Polynomial-Time Identity Testing for Noncommutative Circuits
... (for sparse polynomial identity testing) they used a deterministic finite state automaton to isolate a monomial by designing an automaton which accepts a unique ...a deterministic automaton requires ... See full document
36
Semidiscrete central difference method in time for determining surface temperatures
... Some valid regularizing methods and error estimates for above problem have appeared [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], but most of them only consider the case when x ∈ (0, 1) and cannot obtain the convergence of ... See full document
8
Asymptotic analysis and computations of probability measures
... Gaussian problems (with additive Gaussian noise); for non-linear problems, most important filters, such as the extended Kalman filter (ExKF) and the ensemble Kalman Filter (EnKF) are approximate Gaussian ... See full document
231
Deterministic & Un-deterministic Network Flow Problems
... In practical situations, un-deterministic factors are frequently encountered. This paper represents two types of approaches for a person who wants to plan a tour from one location to another location. First ... See full document
7
Terminal Location Models for Intermodal Transport Network Optimization
... Location problems draw attention of numerous researchers in different ...location problems face theoretical and practical challenges, because every loca- tion problem requires a research approach, ... See full document
9
Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning
... sparse Bayesian inference dealing with constraints (Araki et ...hierarchical Bayesian framework ...the sparse inference. In Bayesian inference, prior probability distributions, namely ... See full document
46
Error Correction for Symbolic and Hybrid Symbolic-Numeric Sparse Interpolation Algorithms.
... The Majority Rule Berlekamp/Massey algorithm of Section 2.5 addresses error correction for linearly generated sequences with exact arithmetic. For characteristic not equal to 2, we have shown optimality in the number of ... See full document
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