34,867 research outputs found
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Multi-agent geographical models integrate very large numbers of spatial
interactions. In order to validate those models large amount of computing is
necessary for their simulation and calibration. Here a new data processing
chain including an automated calibration procedure is experimented on a
computational grid using evolutionary algorithms. This is applied for the first
time to a geographical model designed to simulate the evolution of an early
urban settlement system. The method enables us to reduce the computing time and
provides robust results. Using this method, we identify several parameter
settings that minimise three objective functions that quantify how closely the
model results match a reference pattern. As the values of each parameter in
different settings are very close, this estimation considerably reduces the
initial possible domain of variation of the parameters. The model is thus a
useful tool for further multiple applications on empirical historical
situations
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight
into pathological and physiological alterations of living tissue, with the help
of which researchers hope to predict (local) therapeutic efficacy early and
determine optimal treatment schedule. However, the analysis of qMRI has been
limited to ad-hoc heuristic methods. Our research provides a powerful
statistical framework for image analysis and sheds light on future localized
adaptive treatment regimes tailored to the individual's response. We assume in
an imperfect world we only observe a blurred and noisy version of the
underlying pathological/physiological changes via qMRI, due to measurement
errors or unpredictable influences. We use a hidden Markov random field to
model the spatial dependence in the data and develop a maximum likelihood
approach via the Expectation--Maximization algorithm with stochastic variation.
An important improvement over previous work is the assessment of variability in
parameter estimation, which is the valid basis for statistical inference. More
importantly, we focus on the expected changes rather than image segmentation.
Our research has shown that the approach is powerful in both simulation studies
and on a real dataset, while quite robust in the presence of some model
assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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