11,173 research outputs found
Computational statistics using the Bayesian Inference Engine
This paper introduces the Bayesian Inference Engine (BIE), a general
parallel, optimised software package for parameter inference and model
selection. This package is motivated by the analysis needs of modern
astronomical surveys and the need to organise and reuse expensive derived data.
The BIE is the first platform for computational statistics designed explicitly
to enable Bayesian update and model comparison for astronomical problems.
Bayesian update is based on the representation of high-dimensional posterior
distributions using metric-ball-tree based kernel density estimation. Among its
algorithmic offerings, the BIE emphasises hybrid tempered MCMC schemes that
robustly sample multimodal posterior distributions in high-dimensional
parameter spaces. Moreover, the BIE is implements a full persistence or
serialisation system that stores the full byte-level image of the running
inference and previously characterised posterior distributions for later use.
Two new algorithms to compute the marginal likelihood from the posterior
distribution, developed for and implemented in the BIE, enable model comparison
for complex models and data sets. Finally, the BIE was designed to be a
collaborative platform for applying Bayesian methodology to astronomy. It
includes an extensible object-oriented and easily extended framework that
implements every aspect of the Bayesian inference. By providing a variety of
statistical algorithms for all phases of the inference problem, a scientist may
explore a variety of approaches with a single model and data implementation.
Additional technical details and download details are available from
http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL.Comment: Resubmitted version. Additional technical details and download
details are available from http://www.astro.umass.edu/bie. The BIE is
distributed under the GNU GP
New insight on galaxy structure from GALPHAT I. Motivation, methodology, and benchmarks for Sersic models
We introduce a new galaxy image decomposition tool, GALPHAT (GALaxy
PHotometric ATtributes), to provide full posterior probability distributions
and reliable confidence intervals for all model parameters. GALPHAT is designed
to yield a high speed and accurate likelihood computation, using grid
interpolation and Fourier rotation. We benchmark this approach using an
ensemble of simulated Sersic model galaxies over a wide range of observational
conditions: the signal-to-noise ratio S/N, the ratio of galaxy size to the PSF
and the image size, and errors in the assumed PSF; and a range of structural
parameters: the half-light radius and the Sersic index . We
characterise the strength of parameter covariance in Sersic model, which
increases with S/N and , and the results strongly motivate the need for the
full posterior probability distribution in galaxy morphology analyses and later
inferences.
The test results for simulated galaxies successfully demonstrate that, with a
careful choice of Markov chain Monte Carlo algorithms and fast model image
generation, GALPHAT is a powerful analysis tool for reliably inferring
morphological parameters from a large ensemble of galaxies over a wide range of
different observational conditions. (abridged)Comment: Submitted to MNRAS. The submitted version with high resolution
figures can be downloaded from
http://www.astro.umass.edu/~iyoon/GALPHAT/galphat1.pd
Statistical Modelling of Fishing Activities in the North Atlantic
This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union in the context of the North Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.), are obvious candidates, but one can also consider the season or the actual location of the catch. In all, our database leads to 23 possible regressors, resulting in a set of 8:4£106 possible linear regression models. Prediction of future catches and posterior inference will be based on Bayesian model averaging, using a Markov Chain Monte Carlo Model Composition (MC3) approach. Particular attention is paid to the elicitation of the prior and the prediction of catch for single and aggregated observations.
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