6,491 research outputs found
Towards a Multi-Subject Analysis of Neural Connectivity
Directed acyclic graphs (DAGs) and associated probability models are widely
used to model neural connectivity and communication channels. In many
experiments, data are collected from multiple subjects whose connectivities may
differ but are likely to share many features. In such circumstances it is
natural to leverage similarity between subjects to improve statistical
efficiency. The first exact algorithm for estimation of multiple related DAGs
was recently proposed by Oates et al. 2014; in this letter we present examples
and discuss implications of the methodology as applied to the analysis of fMRI
data from a multi-subject experiment. Elicitation of tuning parameters requires
care and we illustrate how this may proceed retrospectively based on technical
replicate data. In addition to joint learning of subject-specific connectivity,
we allow for heterogeneous collections of subjects and simultaneously estimate
relationships between the subjects themselves. This letter aims to highlight
the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2
Growth Economics and Reality
This paper questions current empirical practice in the study of growth. We argue that much of the modern empirical growth literature is based on assumptions concerning regressors, residuals, and parameters which are implausible both from the perspective of economic theory as well as from the perspective of the historical experiences of the countries under study. A number of these problems are argued to be forms of violations of an exchangeability assumption which underlies standard growth exercises. We show that relaxation of these implausible assumptions can be done by allowing for uncertainty in model specification. Model uncertainty consists of two types: theory uncertainty, which relates to which growth determinants should be included in a model, and heterogeneity uncertainty, which relates to which observations in a data set comprise draws from the same statistical model. We propose ways to account for both theory and heterogeneity uncertainty. Finally, using an explicit decision-theoretic framework, we describe how one can engage in policy-relevant empirical analysis.
Characterization of lead-recycling facility emissions at various workplaces: Major insights for sanitary risks assessment
Most available studies on lead smelter emissions deal with the environmental impact of outdoor particles,
but only a few focus on air quality at workplaces. The objective of this study is to physically and chemically
characterize the Pb-rich particles emitted at different workplaces in a lead recycling plant. A multiscale
characterization was conducted from bulk analysis to the level of individual particles, to assess the
particles properties in relation with Pb speciation and availability. Process PM from various origins were
sampled and then compared; namely Furnace and Refining PM respectively present in the smelter and at
refinery workplaces, Emissions PM present in channeled emissions.
These particles first differed by their morphology and size distribution, with finer particles found in
emissions. Differences observed in chemical composition could be explained by the industrial processes.
All PM contained the same major phases (Pb, PbS, PbO, PbSO4 and PbO·PbSO4) but differed on the nature
and amount of minor phases. Due to high content in PM, Pb concentrations in the CaCl2 extractant reached
relatively high values (40mgLâ1). However, the ratios (soluble/total) of CaCl2 exchangeable Pb were
relatively low (<0.02%) in comparison with Cd (up to 18%). These results highlight the interest to assess
the soluble fractions of all metals (minor and major) and discuss both total metal concentrations and
ratios for risk evaluations. In most cases metal extractability increased with decreasing size of particles,
in particular, lead exchangeability was highest for channeled emissions.
Such type of study could help in the choice of targeted sanitary protection procedures and for further
toxicological investigations. In the present context, particular attention is given to Emissions and Furnace
PM. Moreover, exposure to other metals than Pb should be considered
A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities
Analysis of multivariate data sets from e.g. microarray studies frequently
results in lists of genes which are associated with some response of interest.
The biological interpretation is often complicated by the statistical
instability of the obtained gene lists with respect to sampling variations,
which may partly be due to the functional redundancy among genes, implying that
multiple genes can play exchangeable roles in the cell. In this paper we use
the concept of exchangeability of random variables to model this functional
redundancy and thereby account for the instability attributable to sampling
variations. We present a flexible framework to incorporate the exchangeability
into the representation of lists. The proposed framework supports
straightforward robust comparison between any two lists. It can also be used to
generate new, more stable gene rankings incorporating more information from the
experimental data. Using a microarray data set from lung cancer patients we
show that the proposed method provides more robust gene rankings than existing
methods with respect to sampling variations, without compromising the
biological significance
The Laplace-Jaynes approach to induction
An approach to induction is presented, based on the idea of analysing the
context of a given problem into `circumstances'. This approach, fully Bayesian
in form and meaning, provides a complement or in some cases an alternative to
that based on de Finetti's representation theorem and on the notion of infinite
exchangeability. In particular, it gives an alternative interpretation of those
formulae that apparently involve `unknown probabilities' or `propensities'.
Various advantages and applications of the presented approach are discussed,
especially in comparison to that based on exchangeability. Generalisations are
also discussed.Comment: 38 pages, 1 figure. V2: altered discussion on some points, corrected
typos, added reference
The Counterpart Principle of Analogical Support by Structural Similarity
We propose and investigate an Analogy Principle in the context of Unary Inductive Logic based on a notion of support by structural similarity which is often employed to motivate scientific conjectures
Ambiguity Aversion and Trade
What is the effect of ambiguity aversion on trade? Although in a Bewley's model ambiguity aversion always lead to less trade, in other models this is not always true. However, we show that if the endowments are unambiguous then more ambiguity aversion implies less trade, for a very general class of preferences. The reduction in trade caused by ambiguity aversion can be as severe as to lead to no-trade. In an economy with MEU decision makers, we show that if the aggregate endowment is unanimously unambiguous then every Pareto optima allocation is also unambiguous. We also characterize the situation in which every unanimously unambiguous allocation is Pareto optimal. Finally, we show how our results can be used to explain the home-bias effect. As a useful result for our methods, we also obtain an additivity theorem for CEU and MEU decision makers that does not require comonotonicity. JEL Classification Numbers: D51, D6, D8
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