Unsupervised bayesian convex deconvolution based on a field with an explicit partition function

This paper proposes a non-Gaussian Markov field with a special feature: an explicit partition function. To the best of our knowledge, this is an original contribution. Moreover, the explicit expression of the partition function enables the development of an unsupervised edge-preserving convex deconvolution method. The method is fully Bayesian, and produces an estimate in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov Chain technique. The approach is particularly effective and the computational practicability of the method is shown on a simple simulated example

Image Reconstruction in Optical Interferometry

This tutorial paper describes the problem of image reconstruction from interferometric data with a particular focus on the specific problems encountered at optical (visible/IR) wavelengths. The challenging issues in image reconstruction from interferometric data are introduced in the general framework of inverse problem approach. This framework is then used to describe existing image reconstruction algorithms in radio interferometry and the new methods specifically developed for optical interferometry.Comment: accepted for publication in IEEE Signal Processing Magazin

Bayesian interpretation of periodograms

The usual nonparametric approach to spectral analysis is revisited within the regularization framework. Both usual and windowed periodograms are obtained as the squared modulus of the minimizer of regularized least squares criteria. Then, particular attention is paid to their interpretation within the Bayesian statistical framework. Finally, the question of unsupervised hyperparameter and window selection is addressed. It is shown that maximum likelihood solution is both formally achievable and practically useful

Regulation of Health Plan Provider Networks: Narrow Networks Have Changed Considerably under the Affordable Care Act, but the Trajectory of Regulation Remains Unclear

Health insurance plans with limited networks of providers are common on the Affordable Care Act's (ACA's) health insurance Marketplaces. Recent studies have found that these "narrow network" plans constituted nearly half of all Marketplace offerings in the first two years of coverage, with one analysis concluding that about 90 percent of all consumers had the option of buying such a plan if they chose.Plans with limited networks are not new and are not confined to the Marketplaces. Yet there is reason to believe that they have grown in prevalence partly because of the ACA. Many of the health law's consumer protections--prohibitions on health status underwriting, increased standardization of benefits, a maximum limit on out-of-pocket spending, and the elimination of annual and lifetime limits on benefits, for example--have foreclosed traditional strategies used by insurers to keep costs in check. Meanwhile, other elements of reform, including online Marketplaces that make it easier for consumers to compare plans based on premiums and a financial assistance framework that links the amount of a person's premium tax credit to the cost of the second cheapest plan available to them at the silver metal tier, explicitly encourage insurers to compete on price. These developments appear to have led many insurers to design Marketplace health plans that combined a comparatively low premium with a more restricted choice of providers.Limited network plans might offer value to consumers. Coverage that pairs a low premium with a network that provides meaningful access to health care might meet the needs of many enrollees, no matter the network's overall size. Negotiations between insurers and providers over network participation might encourage more efficient delivery of care. And the power to contract selectively might allow insurers to create networks comprising a subset of providers who meet raised standards of quality, potentially resulting in higher-value care.But these plans also pose risks. A network can be too narrow, jeopardizing the ability of consumers to obtain needed services in a timely manner. This can happen if the network contains an inadequate mix of provider types. For example, a recent examination by Harvard researchers of the network composition of health plans offered on the federal Marketplace during 2015 found that nearly 15 percent of the sampled plans lacked in-network physicians for at least one specialty. Or a network might have an insufficient number of providers: There might be too few physicians who are taking new patients, who are available for an appointment within a reasonable time, or who speak the same language as the enrollee. Certain network limitations also might have the effect of discouraging enrollment by sicker consumers, potentially skewing the risk pool. Plans that provide limited or inadequate access to in-network providers make it more likely that enrollees will obtain care from out-of-network sources, exposing them to significant expenses and the possibility of surprise medical bills.Surveys show that many consumers are open to trading network breadth for a lower premium. They also suggest that, in practice, large numbers of consumers do not find network designs to be transparent. If the features of a plan's network are inadequately explained or its list of participating providers is inaccurate, it might be impossible for consumers to make an informed decision about whether the plan's combination of network and price is right for them.Consumers' experiences with narrow network plans since the ACA's implementation have defied easy characterization. Surveys of the insured, including those with Marketplace coverage, suggest that the vast majority are satisfied with their plan's choice of doctors. Yet anecdotal complaints about networks have proliferated, and the exclusion by some health plans of high-profile hospitals and care facilities has generated media headlines.In light of these developments, and as part of a larger effort to keep pace with changes to the health insurance markets since passage of the ACA, lawmakers and regulators have devoted significant attention to determining how networks should be regulated to ensure they are adequate and transparent. This work has involved efforts to establish or update standards for evaluating the sufficiency of a plan's network, improve the accuracy of provider directories, and protect enrollees from surprise bills from out-of-network providers. This brief offers an overview of state and federal actions that address the first two categories--network standards and provider directories--with a focus on rules that govern plans sold on the ACA's health insurance Marketplaces

Gradient Scan Gibbs Sampler: an efficient algorithm for high-dimensional Gaussian distributions

This paper deals with Gibbs samplers that include high dimensional conditional Gaussian distributions. It proposes an efficient algorithm that avoids the high dimensional Gaussian sampling and relies on a random excursion along a small set of directions. The algorithm is proved to converge, i.e. the drawn samples are asymptotically distributed according to the target distribution. Our main motivation is in inverse problems related to general linear observation models and their solution in a hierarchical Bayesian framework implemented through sampling algorithms. It finds direct applications in semi-blind/unsupervised methods as well as in some non-Gaussian methods. The paper provides an illustration focused on the unsupervised estimation for super-resolution methods.Comment: 18 page

Time delay between the optical and X-ray outbursts in the high mass X-ray transient A0535+26/HDE245770

The optical behaviour of the Be star in the high mass X-ray transient A0535+26/HDE245770 shows that at the periastron typically there is an enhancement in the luminosity of order 0.02 to few tenths mag, and the X-ray outburst happens about 8 days after the periastron. We construct a quantitative model of this event, basing on the a nonstationary accretion disk behavior, connected with a high ellipticity of the orbital motion. The ephemeris used in this paper -- JD$_{\rm opt-outb}$ = JD$_0$(2,444,944) $\pm$ n(111.0 $\pm$ 0.4) days are derived from the orbital period of the system P$_{\rm orb} = 111.0 \pm 0.4$ days, determined by Priedhorsky & Terrell (1983), and from the optical flare of December 5, 1981 (Giovannelli et al., 1985) (here after 811205-E; E stands for the Event occurred at that date) that triggered the subsequent X-ray outburst of December 13, 1981 (Nagase et al., 1982) (here after 811213-E). We explain the observed time delay between the peaks of the optical and X-ray outbursts in this system by the time of radial motion of the matter in the accretion disk, after an increase of the mass flux in the vicinity of a periastral point in the binary. This time is determined by the turbulent viscosity, with the parameter $\alpha=0.1-0.3$. The increase of the mass flux is a sort of flush that reaches the external part of the accretion disk around the neutron star, producing an enhancement in the optical luminosity. The consequent X-ray flare happens when the matter reaches the hot central parts of the accretion disk, and the neutron star surface.Comment: 30 pages, 15 figures, with correction in abstrac

Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making

We describe regularized methods for image reconstruction and focus on the question of hyperparameter and instrument parameter estimation, i.e. unsupervised and myopic problems. We developed a Bayesian framework that is based on the \post density for all unknown quantities, given the observations. This density is explored by a Markov Chain Monte-Carlo sampling technique based on a Gibbs loop and including a Metropolis-Hastings step. The numerical evaluation relies on the SPIRE instrument of the Herschel observatory. Using simulated and real observations, we show that the hyperparameters and instrument parameters are correctly estimated, which opens up many perspectives for imaging in astrophysics