2,130 research outputs found
Certified dimension reduction in nonlinear Bayesian inverse problems
We propose a dimension reduction technique for Bayesian inverse problems with
nonlinear forward operators, non-Gaussian priors, and non-Gaussian observation
noise. The likelihood function is approximated by a ridge function, i.e., a map
which depends non-trivially only on a few linear combinations of the
parameters. We build this ridge approximation by minimizing an upper bound on
the Kullback--Leibler divergence between the posterior distribution and its
approximation. This bound, obtained via logarithmic Sobolev inequalities,
allows one to certify the error of the posterior approximation. Computing the
bound requires computing the second moment matrix of the gradient of the
log-likelihood function. In practice, a sample-based approximation of the upper
bound is then required. We provide an analysis that enables control of the
posterior approximation error due to this sampling. Numerical and theoretical
comparisons with existing methods illustrate the benefits of the proposed
methodology
DPO - Denoising, Deconvolving, and Decomposing Photon Observations
The analysis of astronomical images is a non-trivial task. The D3PO algorithm
addresses the inference problem of denoising, deconvolving, and decomposing
photon observations. Its primary goal is the simultaneous but individual
reconstruction of the diffuse and point-like photon flux given a single photon
count image, where the fluxes are superimposed. In order to discriminate
between these morphologically different signal components, a probabilistic
algorithm is derived in the language of information field theory based on a
hierarchical Bayesian parameter model. The signal inference exploits prior
information on the spatial correlation structure of the diffuse component and
the brightness distribution of the spatially uncorrelated point-like sources. A
maximum a posteriori solution and a solution minimizing the Gibbs free energy
of the inference problem using variational Bayesian methods are discussed.
Since the derivation of the solution is not dependent on the underlying
position space, the implementation of the D3PO algorithm uses the NIFTY package
to ensure applicability to various spatial grids and at any resolution. The
fidelity of the algorithm is validated by the analysis of simulated data,
including a realistic high energy photon count image showing a 32 x 32 arcmin^2
observation with a spatial resolution of 0.1 arcmin. In all tests the D3PO
algorithm successfully denoised, deconvolved, and decomposed the data into a
diffuse and a point-like signal estimate for the respective photon flux
components.Comment: 22 pages, 8 figures, 2 tables, accepted by Astronomy & Astrophysics;
refereed version, 1 figure added, results unchanged, software available at
http://www.mpa-garching.mpg.de/ift/d3po
Deformable kernels for early vision
Early vision algorithms often have a first stage of linear-filtering that `extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. A technique is presented that allows: 1) computing the best approximation of a given family using linear combinations of a small number of `basis' functions; and 2) describing all finite-dimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations. The relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multi-scale 2D edge-detection kernels. The implementation issues are also discussed
A bias to CMB lensing measurements from the bispectrum of large-scale structure
The rapidly improving precision of measurements of gravitational lensing of
the Cosmic Microwave Background (CMB) also requires a corresponding increase in
the precision of theoretical modeling. A commonly made approximation is to
model the CMB deflection angle or lensing potential as a Gaussian random field.
In this paper, however, we analytically quantify the influence of the
non-Gaussianity of large-scale structure lenses, arising from nonlinear
structure formation, on CMB lensing measurements. In particular, evaluating the
impact of the non-zero bispectrum of large-scale structure on the relevant CMB
four-point correlation functions, we find that there is a bias to estimates of
the CMB lensing power spectrum. For temperature-based lensing reconstruction
with CMB Stage-III and Stage-IV experiments, we find that this lensing power
spectrum bias is negative and is of order one percent of the signal. This
corresponds to a shift of multiple standard deviations for these upcoming
experiments. We caution, however, that our numerical calculation only evaluates
two of the largest bias terms and thus only provides an approximate estimate of
the full bias. We conclude that further investigation into lensing biases from
nonlinear structure formation is required and that these biases should be
accounted for in future lensing analyses.Comment: 15+19 pages, 9 figures. Comments welcom
Approximation based tree regular model checking
International audienceThis paper addresses the following general problem of tree regular model-checking: decide whether where is the reflexive and transitive closure of a successor relation induced by a term rewriting system , and and are both regular tree languages. We develop an automatic approximation-based technique to handle this -- undecidable in general -- problem in most practical cases, extending a recent work by Feuillade, Genet and Viet Triem Tong. We also make this approach fully automatic for practical validation of security protocols
Conditions for Existence of Dual Certificates in Rank-One Semidefinite Problems
Several signal recovery tasks can be relaxed into semidefinite programs with
rank-one minimizers. A common technique for proving these programs succeed is
to construct a dual certificate. Unfortunately, dual certificates may not exist
under some formulations of semidefinite programs. In order to put problems into
a form where dual certificate arguments are possible, it is important to
develop conditions under which the certificates exist. In this paper, we
provide an example where dual certificates do not exist. We then present a
completeness condition under which they are guaranteed to exist. For programs
that do not satisfy the completeness condition, we present a completion process
which produces an equivalent program that does satisfy the condition. The
important message of this paper is that dual certificates may not exist for
semidefinite programs that involve orthogonal measurements with respect to
positive-semidefinite matrices. Such measurements can interact with the
positive-semidefinite constraint in a way that implies additional linear
measurements. If these additional measurements are not included in the problem
formulation, then dual certificates may fail to exist. As an illustration, we
present a semidefinite relaxation for the task of finding the sparsest element
in a subspace. One formulation of this program does not admit dual
certificates. The completion process produces an equivalent formulation which
does admit dual certificates
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