7,310 research outputs found
Expectation Propagation for Poisson Data
The Poisson distribution arises naturally when dealing with data involving
counts, and it has found many applications in inverse problems and imaging. In
this work, we develop an approximate Bayesian inference technique based on
expectation propagation for approximating the posterior distribution formed
from the Poisson likelihood function and a Laplace type prior distribution,
e.g., the anisotropic total variation prior. The approach iteratively yields a
Gaussian approximation, and at each iteration, it updates the Gaussian
approximation to one factor of the posterior distribution by moment matching.
We derive explicit update formulas in terms of one-dimensional integrals, and
also discuss stable and efficient quadrature rules for evaluating these
integrals. The method is showcased on two-dimensional PET images.Comment: 25 pages, to be published at Inverse Problem
Simulated single molecule microscopy with SMeagol
SMeagol is a software tool to simulate highly realistic microscopy data based
on spatial systems biology models, in order to facilitate development,
validation, and optimization of advanced analysis methods for live cell single
molecule microscopy data. Availability and Implementation: SMeagol runs on
Matlab R2014 and later, and uses compiled binaries in C for reaction-diffusion
simulations. Documentation, source code, and binaries for recent versions of
Mac OS, Windows, and Ubuntu Linux can be downloaded from
http://smeagol.sourceforge.net.Comment: v2: 14 pages including supplementary text. Pre-copyedited,
author-produced version of an application note published in Bioinformatics
following peer review. The version of record, and additional supplementary
material is available online at:
https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw10
Photometric redshifts and quasar probabilities from a single, data-driven generative model
We describe a technique for simultaneously classifying and estimating the
redshift of quasars. It can separate quasars from stars in arbitrary redshift
ranges, estimate full posterior distribution functions for the redshift, and
naturally incorporate flux uncertainties, missing data, and multi-wavelength
photometry. We build models of quasars in flux-redshift space by applying the
extreme deconvolution technique to estimate the underlying density. By
integrating this density over redshift one can obtain quasar flux-densities in
different redshift ranges. This approach allows for efficient, consistent, and
fast classification and photometric redshift estimation. This is achieved by
combining the speed obtained by choosing simple analytical forms as the basis
of our density model with the flexibility of non-parametric models through the
use of many simple components with many parameters. We show that this technique
is competitive with the best photometric quasar classification
techniques---which are limited to fixed, broad redshift ranges and high
signal-to-noise ratio data---and with the best photometric redshift techniques
when applied to broadband optical data. We demonstrate that the inclusion of UV
and NIR data significantly improves photometric quasar--star separation and
essentially resolves all of the redshift degeneracies for quasars inherent to
the ugriz filter system, even when included data have a low signal-to-noise
ratio. For quasars spectroscopically confirmed by the SDSS 84 and 97 percent of
the objects with GALEX UV and UKIDSS NIR data have photometric redshifts within
0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about
a factor of three improvement over ugriz-only photometric redshifts. Our code
to calculate quasar probabilities and redshift probability distributions is
publicly available
Seeing into Darkness: Scotopic Visual Recognition
Images are formed by counting how many photons traveling from a given set of
directions hit an image sensor during a given time interval. When photons are
few and far in between, the concept of `image' breaks down and it is best to
consider directly the flow of photons. Computer vision in this regime, which we
call `scotopic', is radically different from the classical image-based paradigm
in that visual computations (classification, control, search) have to take
place while the stream of photons is captured and decisions may be taken as
soon as enough information is available. The scotopic regime is important for
biomedical imaging, security, astronomy and many other fields. Here we develop
a framework that allows a machine to classify objects with as few photons as
possible, while maintaining the error rate below an acceptable threshold. A
dynamic and asymptotically optimal speed-accuracy tradeoff is a key feature of
this framework. We propose and study an algorithm to optimize the tradeoff of a
convolutional network directly from lowlight images and evaluate on simulated
images from standard datasets. Surprisingly, scotopic systems can achieve
comparable classification performance as traditional vision systems while using
less than 0.1% of the photons in a conventional image. In addition, we
demonstrate that our algorithms work even when the illuminance of the
environment is unknown and varying. Last, we outline a spiking neural network
coupled with photon-counting sensors as a power-efficient hardware realization
of scotopic algorithms.Comment: 23 pages, 6 figure
Automated reliability assessment for spectroscopic redshift measurements
We present a new approach to automate the spectroscopic redshift reliability
assessment based on machine learning (ML) and characteristics of the redshift
probability density function (PDF).
We propose to rephrase the spectroscopic redshift estimation into a Bayesian
framework, in order to incorporate all sources of information and uncertainties
related to the redshift estimation process, and produce a redshift posterior
PDF that will be the starting-point for ML algorithms to provide an automated
assessment of a redshift reliability.
As a use case, public data from the VIMOS VLT Deep Survey is exploited to
present and test this new methodology. We first tried to reproduce the existing
reliability flags using supervised classification to describe different types
of redshift PDFs, but due to the subjective definition of these flags, soon
opted for a new homogeneous partitioning of the data into distinct clusters via
unsupervised classification. After assessing the accuracy of the new clusters
via resubstitution and test predictions, unlabelled data from preliminary mock
simulations for the Euclid space mission are projected into this mapping to
predict their redshift reliability labels.Comment: Submitted on 02 June 2017 (v1). Revised on 08 September 2017 (v2).
Latest version 28 September 2017 (this version v3
4-D Tomographic Inference: Application to SPECT and MR-driven PET
Emission tomographic imaging is framed in the Bayesian and information theoretic framework. The first part of the thesis is inspired by the new possibilities offered by PET-MR systems, formulating models and algorithms for 4-D tomography and for the integration of information from multiple imaging modalities. The second part of the thesis extends the models described in the first part, focusing on the imaging hardware. Three key aspects for the design of new imaging systems are investigated: criteria and efficient algorithms for the optimisation and real-time adaptation of the parameters of the imaging hardware; learning the characteristics of the imaging hardware; exploiting the rich information provided by depthof- interaction (DOI) and energy resolving devices. The document concludes with the description of the NiftyRec software toolkit, developed to enable 4-D multi-modal tomographic inference
Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting
While considerable advance has been made to account for statistical
uncertainties in astronomical analyses, systematic instrumental uncertainties
have been generally ignored. This can be crucial to a proper interpretation of
analysis results because instrumental calibration uncertainty is a form of
systematic uncertainty. Ignoring it can underestimate error bars and introduce
bias into the fitted values of model parameters. Accounting for such
uncertainties currently requires extensive case-specific simulations if using
existing analysis packages. Here we present general statistical methods that
incorporate calibration uncertainties into spectral analysis of high-energy
data. We first present a method based on multiple imputation that can be
applied with any fitting method, but is necessarily approximate. We then
describe a more exact Bayesian approach that works in conjunction with a Markov
chain Monte Carlo based fitting. We explore methods for improving computational
efficiency, and in particular detail a method of summarizing calibration
uncertainties with a principal component analysis of samples of plausible
calibration files. This method is implemented using recently codified Chandra
effective area uncertainties for low-resolution spectral analysis and is
verified using both simulated and actual Chandra data. Our procedure for
incorporating effective area uncertainty is easily generalized to other types
of calibration uncertainties.Comment: 61 pages double spaced, 8 figures, accepted for publication in Ap
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