59,839 research outputs found
Revisiting the Gelman-Rubin Diagnostic
Gelman and Rubin's (1992) convergence diagnostic is one of the most popular
methods for terminating a Markov chain Monte Carlo (MCMC) sampler. Since the
seminal paper, researchers have developed sophisticated methods for estimating
variance of Monte Carlo averages. We show that these estimators find immediate
use in the Gelman-Rubin statistic, a connection not previously established in
the literature. We incorporate these estimators to upgrade both the univariate
and multivariate Gelman-Rubin statistics, leading to improved stability in MCMC
termination time. An immediate advantage is that our new Gelman-Rubin statistic
can be calculated for a single chain. In addition, we establish a one-to-one
relationship between the Gelman-Rubin statistic and effective sample size.
Leveraging this relationship, we develop a principled termination criterion for
the Gelman-Rubin statistic. Finally, we demonstrate the utility of our improved
diagnostic via examples
Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
Current reporting of results based on Markov chain Monte Carlo computations
could be improved. In particular, a measure of the accuracy of the resulting
estimates is rarely reported. Thus we have little ability to objectively assess
the quality of the reported estimates. We address this issue in that we discuss
why Monte Carlo standard errors are important, how they can be easily
calculated in Markov chain Monte Carlo and how they can be used to decide when
to stop the simulation. We compare their use to a popular alternative in the
context of two examples.Comment: Published in at http://dx.doi.org/10.1214/08-STS257 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Group Analysis of Self-organizing Maps based on Functional MRI using Restricted Frechet Means
Studies of functional MRI data are increasingly concerned with the estimation
of differences in spatio-temporal networks across groups of subjects or
experimental conditions. Unsupervised clustering and independent component
analysis (ICA) have been used to identify such spatio-temporal networks. While
these approaches have been useful for estimating these networks at the
subject-level, comparisons over groups or experimental conditions require
further methodological development. In this paper, we tackle this problem by
showing how self-organizing maps (SOMs) can be compared within a Frechean
inferential framework. Here, we summarize the mean SOM in each group as a
Frechet mean with respect to a metric on the space of SOMs. We consider the use
of different metrics, and introduce two extensions of the classical sum of
minimum distance (SMD) between two SOMs, which take into account the
spatio-temporal pattern of the fMRI data. The validity of these methods is
illustrated on synthetic data. Through these simulations, we show that the
three metrics of interest behave as expected, in the sense that the ones
capturing temporal, spatial and spatio-temporal aspects of the SOMs are more
likely to reach significance under simulated scenarios characterized by
temporal, spatial and spatio-temporal differences, respectively. In addition, a
re-analysis of a classical experiment on visually-triggered emotions
demonstrates the usefulness of this methodology. In this study, the
multivariate functional patterns typical of the subjects exposed to pleasant
and unpleasant stimuli are found to be more similar than the ones of the
subjects exposed to emotionally neutral stimuli. Taken together, these results
indicate that our proposed methods can cast new light on existing data by
adopting a global analytical perspective on functional MRI paradigms.Comment: 23 pages, 5 figures, 4 tables. Submitted to Neuroimag
Deep Neural Networks for Energy and Position Reconstruction in EXO-200
We apply deep neural networks (DNN) to data from the EXO-200 experiment. In
the studied cases, the DNN is able to reconstruct the relevant parameters -
total energy and position - directly from raw digitized waveforms, with minimal
exceptions. For the first time, the developed algorithms are evaluated on real
detector calibration data. The accuracy of reconstruction either reaches or
exceeds what was achieved by the conventional approaches developed by EXO-200
over the course of the experiment. Most existing DNN approaches to event
reconstruction and classification in particle physics are trained on Monte
Carlo simulated events. Such algorithms are inherently limited by the accuracy
of the simulation. We describe a unique approach that, in an experiment such as
EXO-200, allows to successfully perform certain reconstruction and analysis
tasks by training the network on waveforms from experimental data, either
reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure
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