22,306 research outputs found
Eliciting density ratio classes
AbstractThe probability distributions of uncertain quantities needed for predictive modelling and decision support are frequently elicited from subject matter experts. However, experts are often uncertain about quantifying their beliefs using precise probability distributions. Therefore, it seems natural to describe their uncertain beliefs using sets of probability distributions. There are various possible structures, or classes, for defining set membership of continuous random variables. The Density Ratio Class has desirable properties, but there is no established procedure for eliciting this class. Thus, we propose a method for constructing Density Ratio Classes that builds on conventional quantile or probability elicitation, but allows the expert to state intervals for these quantities. Parametric shape functions, ideally also suggested by the expert, are then used to bound the nonparametric set of shapes of densities that belong to the class and are compatible with the stated intervals. This leads to a natural metric for the size of the class based on the ratio of the total areas under upper and lower bounding shape functions. This ratio will be determined by the characteristics of the shape functions, the scatter of the elicited values, and the explicit expert imprecision, as characterized by the width of the stated intervals. We provide some examples, both didactic and real, and conclude with recommendations for the further development and application of the Density Ratio Class
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Oxylipins in triglyceride-rich lipoproteins of dyslipidemic subjects promote endothelial inflammation following a high fat meal.
Elevated triglyceride-rich lipoproteins (TGRL) in circulation is a risk factor for atherosclerosis. TGRL from subjects consuming a high saturated fat test meal elicited a variable inflammatory response in TNFα-stimulated endothelial cells (EC) that correlated strongly with the polyunsaturated fatty acid (PUFA) content. This study investigates how the relative abundance of oxygenated metabolites of PUFA, oxylipins, is altered in TGRL postprandially, and how these changes promote endothelial inflammation. Human aortic EC were stimulated with TNFα and treated with TGRL, isolated from subjects' plasma at fasting and 3.5 hrs postprandial to a test meal high in saturated fat. Endothelial VCAM-1 surface expression stimulated by TNFα provided a readout for atherogenic inflammation. Concentrations of esterified and non-esterified fatty acids and oxylipins in TGRL were quantified by mass spectrometry. Dyslipidemic subjects produced TGRL that increased endothelial VCAM-1 expression by ≥35%, and exhibited impaired fasting lipogenesis activity and a shift in soluble epoxide hydrolase and lipoxygenase activity. Pro-atherogenic TGRL were enriched in eicosapentaenoic acid metabolites and depleted in esterified C18-PUFA-derived diols. Abundance of these metabolites was strongly predictive of VCAM-1 expression. We conclude the altered metabolism in dyslipidemic subjects produces TGRL with a unique oxylipin signature that promotes a pro-atherogenic endothelial phenotype
Detecting Functional Requirements Inconsistencies within Multi-teams Projects Framed into a Model-based Web Methodology
One of the most essential processes within the software project life cycle is the REP (Requirements
Engineering Process) because it allows specifying the software product requirements. This specification
should be as consistent as possible because it allows estimating in a suitable manner the effort required to
obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed
and heterogeneous projects with multiple analyst teams and high integration between functional modules.
This paper presents an approach for the systematic conciliation of functional requirements in big projects
dealing with a web model-based approach and how this approach may be implemented in the context of the
NDT (Navigational Development Techniques): a web methodology. This paper also describes the empirical
evaluation in the CALIPSOneo project by analyzing the improvements obtained with our approach.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction
Stimulus dimensionality-reduction methods in neuroscience seek to identify a
low-dimensional space of stimulus features that affect a neuron's probability
of spiking. One popular method, known as maximally informative dimensions
(MID), uses an information-theoretic quantity known as "single-spike
information" to identify this space. Here we examine MID from a model-based
perspective. We show that MID is a maximum-likelihood estimator for the
parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical
single-spike information corresponds to the normalized log-likelihood under a
Poisson model. This equivalence implies that MID does not necessarily find
maximally informative stimulus dimensions when spiking is not well described as
Poisson. We provide several examples to illustrate this shortcoming, and derive
a lower bound on the information lost when spiking is Bernoulli in discrete
time bins. To overcome this limitation, we introduce model-based dimensionality
reduction methods for neurons with non-Poisson firing statistics, and show that
they can be framed equivalently in likelihood-based or information-theoretic
terms. Finally, we show how to overcome practical limitations on the number of
stimulus dimensions that MID can estimate by constraining the form of the
non-parametric nonlinearity in an LNP model. We illustrate these methods with
simulations and data from primate visual cortex
The Intracluster Plasma: a Universal Pressure Profile?
The pressure profiles of the Intracluster Plasma in galaxy clusters show a
wide variance when observed in X rays at low redshifts z<0.2. We find the
profiles to follow two main patterns, featuring either a steep or a shallow
shape throughout both core and outskirts. We trace these shapes back to a
physical dichotomy of clusters into two classes, marked by either low entropy
(LE) or high entropy (HE) throughout. From X-ray observations and
Sunyaev-Zel'dovich stacked data at higher 0.2<z<0.4, we elicit evidence of an
increasing abundance of HEs relative to LEs. We propose this to constitute a
systematic trend toward high z; specifically, we predict the pressure profiles
to converge into a truly universal HE-like template for z>0.5. We submit our
physical templates and converging trend for further observational tests, in
view of the current and upcoming measurements of individual, stacked, and
integrated Sunyaev-Zel'dovich signals.Comment: 5 pages, 2 figures. Typos-corrected. Accepted by ApJ
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