Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient sampling of high-dimensional Gaussian fields: the
  non-stationary / non-sparse case   This paper is devoted to the problem of sampling Gaussian fields in high
dimension. Solutions exist for two specific structures of inverse covariance :
sparse and circulant. The proposed approach is valid in a more general case and
especially as it emerges in inverse problems. It relies on a
perturbation-optimization principle: adequate stochastic perturbation of a
criterion and optimization of the perturbed criterion. It is shown that the
criterion minimizer is a sample of the target density. The motivation in
inverse problems is related to general (non-convolutive) linear observation
models and their resolution in a Bayesian framework implemented through
sampling algorithms when existing samplers are not feasible. It finds a direct
application in myopic and/or unsupervised inversion as well as in some
non-Gaussian inversion. An illustration focused on hyperparameter estimation
for super-resolution problems assesses the effectiveness of the proposed
approach.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th