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To validate pollution data, subject-matter experts in Airpl (an organization
that maintains a network of air pollution monitoring stations in western
France) daily perform visual examinations of the data and check their
consistency. In this paper, we describe these visual examinations and
propose a formalization for this problem. The examinations consist
in comparisons of so-called shorth intervals so we build a statistical
test that compares such intervals in a nonparametric regression model.
This...
To validate pollution data, subject-matter experts in Airpl (an organization that maintains a network of air pollution monitoring stations in western France) daily perform visual examinations of the data and check their consistency. In this paper, we describe these visual examinations and propose a formalization for this problem. The examinations consist in comparisons of so-called shorth intervals so we build a statistical test that compares such intervals in a nonparametric regression model. This...
Given a fixed dependency graph that describes a Bayesian network of binary variables , our main result is a tight bound on the mutual information of an observed subset of the variables . Our bound depends on certain quantities that can be computed from the connective structure of the nodes in . Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.
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