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B.Sc. degree from Federal University of Rio de Janeiro (UFRJ) in Electrical and Computer Engineering (ECE); M.Sc. from the Instituto Tecnológico de Aeronáutica (ITA), Sao Jose ...
SIAMSC
2011
13 years 6 months ago
2011
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
CORR
2011
Springer
13 years 6 months ago
2011
Springer
Historical linguistics aims at inferring the most likely language phylogenetic tree starting from information concerning the evolutionary relatedness of languages. The available i...
SIGPRO
2010
13 years 9 months ago
2010
Total Variation (TV) regularization is a popular method for solving a wide variety of inverse problems in image processing. In order to optimize the reconstructed image, it is imp...
SIAMSC
2010
13 years 9 months ago
2010
A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for efficient solution of statistical inverse problems governed b...
TIP
1998
13 years 11 months ago
1998
Abstract—We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse...
JAT
2008
13 years 11 months ago
2008
Variable Hilbert scales are an important tool for the recent analysis of inverse problems in Hilbert spaces, as these constitute a way to describe smoothness of objects other than ...
CORR
2010
Springer
13 years 11 months ago
2010
Springer
A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP...
METMBS
2003
14 years 24 days ago
2003
— We describe a stochastic optimization method that can be used to solve inverse problems in epidemic modelling. Although in general it cannot be expected that these inverse prob...
SC
2005
ACM
14 years 5 months ago
2005
ACM
In contrast to traditional terascale simulations that have known, fixed data inputs, dynamic data-driven (DDD) applications are characterized by unknown data and informed by dynam...