Belief Propagation for Linear Programming

Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve a special class of Linear Programming (LP) problems. For this class of problems, MAP inference can be stated as an integer LP with an LP relaxation that coincides with minimization of the BFE at ``zero temperature". We generalize these prior results and establish a tight characterization of the LP problems that can be formulated as an equivalent LP relaxation of MAP inference. Moreover, we suggest an efficient, iterative annealing BP algorithm for solving this broader class of LP problems. We demonstrate the algorithm's performance on a set of weighted matching problems by using it as a cutting plane method to solve a sequence of LPs tightened by adding ``blossom'' inequalities.Comment: To appear in ISIT 201

Death Watch

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Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This manuscript develops a class of highly scalable Nearest Neighbor Gaussian Process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive United States Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods

Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly $37{,}000$ one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only $28$ of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models

In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete

Three-Dimensional Relativistic MHD Simulations of the Kelvin-Helmholtz Instability: Magnetic Field Amplification by a Turbulent Dynamo

Magnetic field strengths inferred for relativistic outflows including gamma-ray bursts (GRB) and active galactic nuclei (AGN) are larger than naively expected by orders of magnitude. We present three-dimensional relativistic magnetohydrodynamics (MHD) simulations demonstrating amplification and saturation of magnetic field by a macroscopic turbulent dynamo triggered by the Kelvin-Helmholtz shear instability. We find rapid growth of electromagnetic energy due to the stretching and folding of field lines in the turbulent velocity field resulting from non-linear development of the instability. Using conditions relevant for GRB internal shocks and late phases of GRB afterglow, we obtain amplification of the electromagnetic energy fraction to $\epsilon_B \sim 5 \times 10^{-3}$. This value decays slowly after the shear is dissipated and appears to be largely independent of the initial field strength. The conditions required for operation of the dynamo are the presence of velocity shear and some seed magnetization both of which are expected to be commonplace. We also find that the turbulent kinetic energy spectrum for the case studied obeys Kolmogorov's 5/3 law and that the electromagnetic energy spectrum is essentially flat with the bulk of the electromagnetic energy at small scales.Comment: accepted for publication in ApJL; high-resolution version available at http://cosmo.nyu.edu/~wqzhang/publications/kh.pdf; movies of simulations available at http://cosmo.nyu.edu/~wqzhang/movies

Optical Discovery of Probable Stellar Tidal Disruption Flares

Using archival Sloan Digital Sky Survey (SDSS) multi-epoch imaging data (Stripe 82), we have searched for the tidal disruption of stars by supermassive black holes in non-active galaxies. Two candidate tidal disruption events (TDEs) are identified. The TDE flares have optical blackbody temperatures of 2 × 10^4 K and observed peak luminosities of M_g = –18.3 and –20.4 (νL_ν = 5 × 10^(42), 4 × 10^(43) erg s^(–1), in the rest frame); their cooling rates are very low, qualitatively consistent with expectations for tidal disruption flares. The properties of the TDE candidates are examined using (1) SDSS imaging to compare them to other flares observed in the search, (2) UV emission measured by GALEX, and (3) spectra of the hosts and of one of the flares. Our pipeline excludes optically identifiable AGN hosts, and our variability monitoring over nine years provides strong evidence that these are not flares in hidden AGNs. The spectra and color evolution of the flares are unlike any SN observed to date, their strong late-time UV emission is particularly distinctive, and they are nuclear at high resolution arguing against these being first cases of a previously unobserved class of SNe or more extreme examples of known SN types. Taken together, the observed properties are difficult to reconcile with an SN or an AGN-flare explanation, although an entirely new process specific to the inner few hundred parsecs of non-active galaxies cannot be excluded. Based on our observed rate, we infer that hundreds or thousands of TDEs will be present in current and next-generation optical synoptic surveys. Using the approach outlined here, a TDE candidate sample with O(1) purity can be selected using geometric resolution and host and flare color alone, demonstrating that a campaign to create a large sample of TDEs, with immediate and detailed multi-wavelength follow-up, is feasible. A by-product of this work is quantification of the power spectrum of extreme flares in AGNs