9,747 research outputs found
Quantifying Uncertainty in High Dimensional Inverse Problems by Convex Optimisation
Inverse problems play a key role in modern image/signal processing methods.
However, since they are generally ill-conditioned or ill-posed due to lack of
observations, their solutions may have significant intrinsic uncertainty.
Analysing and quantifying this uncertainty is very challenging, particularly in
high-dimensional problems and problems with non-smooth objective functionals
(e.g. sparsity-promoting priors). In this article, a series of strategies to
visualise this uncertainty are presented, e.g. highest posterior density
credible regions, and local credible intervals (cf. error bars) for individual
pixels and superpixels. Our methods support non-smooth priors for inverse
problems and can be scaled to high-dimensional settings. Moreover, we present
strategies to automatically set regularisation parameters so that the proposed
uncertainty quantification (UQ) strategies become much easier to use. Also,
different kinds of dictionaries (complete and over-complete) are used to
represent the image/signal and their performance in the proposed UQ methodology
is investigated.Comment: 5 pages, 5 figure
Maximum-a-posteriori estimation with Bayesian confidence regions
Solutions to inverse problems that are ill-conditioned or ill-posed may have
significant intrinsic uncertainty. Unfortunately, analysing and quantifying
this uncertainty is very challenging, particularly in high-dimensional
problems. As a result, while most modern mathematical imaging methods produce
impressive point estimation results, they are generally unable to quantify the
uncertainty in the solutions delivered. This paper presents a new general
methodology for approximating Bayesian high-posterior-density credibility
regions in inverse problems that are convex and potentially very
high-dimensional. The approximations are derived by using recent concentration
of measure results related to information theory for log-concave random
vectors. A remarkable property of the approximations is that they can be
computed very efficiently, even in large-scale problems, by using standard
convex optimisation techniques. In particular, they are available as a
by-product in problems solved by maximum-a-posteriori estimation. The
approximations also have favourable theoretical properties, namely they
outer-bound the true high-posterior-density credibility regions, and they are
stable with respect to model dimension. The proposed methodology is illustrated
on two high-dimensional imaging inverse problems related to tomographic
reconstruction and sparse deconvolution, where the approximations are used to
perform Bayesian hypothesis tests and explore the uncertainty about the
solutions, and where proximal Markov chain Monte Carlo algorithms are used as
benchmark to compute exact credible regions and measure the approximation
error
Revisiting maximum-a-posteriori estimation in log-concave models
Maximum-a-posteriori (MAP) estimation is the main Bayesian estimation
methodology in imaging sciences, where high dimensionality is often addressed
by using Bayesian models that are log-concave and whose posterior mode can be
computed efficiently by convex optimisation. Despite its success and wide
adoption, MAP estimation is not theoretically well understood yet. The
prevalent view in the community is that MAP estimation is not proper Bayesian
estimation in a decision-theoretic sense because it does not minimise a
meaningful expected loss function (unlike the minimum mean squared error (MMSE)
estimator that minimises the mean squared loss). This paper addresses this
theoretical gap by presenting a decision-theoretic derivation of MAP estimation
in Bayesian models that are log-concave. A main novelty is that our analysis is
based on differential geometry, and proceeds as follows. First, we use the
underlying convex geometry of the Bayesian model to induce a Riemannian
geometry on the parameter space. We then use differential geometry to identify
the so-called natural or canonical loss function to perform Bayesian point
estimation in that Riemannian manifold. For log-concave models, this canonical
loss is the Bregman divergence associated with the negative log posterior
density. We then show that the MAP estimator is the only Bayesian estimator
that minimises the expected canonical loss, and that the posterior mean or MMSE
estimator minimises the dual canonical loss. We also study the question of MAP
and MSSE estimation performance in large scales and establish a universal bound
on the expected canonical error as a function of dimension, offering new
insights into the good performance observed in convex problems. These results
provide a new understanding of MAP and MMSE estimation in log-concave settings,
and of the multiple roles that convex geometry plays in imaging problems.Comment: Accepted for publication in SIAM Imaging Science
Why the effective-mass approximation works so well for nano-structures
The reason why the effective-mass approximation, derived for wave packets
constructed from infinite-periodic-systems' wave functions, works so well with
nanoscopic structures, has been an enigma and a challenge for theorists. To
explain and clarify this issue, we re-derive the effective-mass approximation
in the framework of the theory of finite periodic systems, i.e., using energy
eigenvalues and fast-varying eigenfunctions, obtained with analytical methods
where the finiteness of the number of primitive cells per layer, in the
direction of growth, is a prerequisite and an essential condition. This
derivation justifies and explains why the effective-mass approximation works so
well for nano-structures. We show also with explicit optical-response
calculations that the rapidly varying eigenfunctions
of the one-band wave functions
, can be safely dropped out for the calculation of
inter-band transition matrix elements.Comment: 6 page
New approach to study light-emission of periodic structures. Unveiling novel surface-states effects
An accurate approach to calculate the optical response of periodic structures
is proposed. Using the genuine superlattice eigenfunctions and energy
eigenvalues, the eigenfunctions parity symmetries, the subband symmetries and
the detached surface energy levels, we report new optical-transition selection
rules and explicit optical-response calculations. Observed transitions that
were considered forbidden, become allowed and interesting optical-spectra
effects emerge as fingerprints of intra-subband and surface states. The
unexplained groups and isolated narrow peaks observed in high resolution
blue-laser spectra, by Nakamura et al., are now fully explained and faithfully
reproducedComment: 5 pages, 6 figure
II—Resemblance Nominalism, Conjunctions and Truthmakers
The resemblance nominalist says that the truthmaker of 〈Socrates is white〉 ultimately involves only concrete particulars that resemble each other. Furthermore he also says that Socrates and Plato are the truthmakers of 〈Socrates resembles Plato〉, and Socrates and Aristotle those of 〈Socrates resembles Aristotle〉. But this, combined with a principle about the truthmakers of conjunctions, leads to the apparently implausible conclusion that 〈Socrates resembles Plato and Socrates resembles Aristotle〉 and 〈Socrates resembles Plato and Plato resembles Aristotle〉 have the same truthmakers, namely, Socrates, Plato and Aristotle. I shall argue that the resemblance nominalist can say that those conjunctions have the same truthmakers but these truthmakers make them true in different ways. I shall also use this view to account for the truthmakers of propositions like 〈Socrates is white〉, and respond to previous objections by Cian Dorr and Jessica Wilson
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