5,035 research outputs found
Taming Graphical Modeling
Visual models help to understand complex systems. However, with the user interaction paradigms established today, activities such as creating, maintaining or browsing visual models can be very tedious. Valuable engineering time is wasted with archaic activities such as manual placement and routing of nodes and edges. This report presents an approach to enhance productivity by focusing on the pragmatics of model-based design. Our contribution is twofold: First, the concept of meta layout enables the synthesis of different diagrammatic views on graphical models. This modularly employs sophisticated layout algorithms, closing the gap between MDE and graph drawing theory. Second, a view management logic harnesses this auto layout to present customized views on models. These concepts have been implemented in the open source Kiel Integrated Environment for Layout Eclipse Rich Client (KIELER). Two applications---editing and simulation---illustrate how view management helps to increase developer productivity and tame model complexity
GMF: A Model Migration Case for the Transformation Tool Contest
Using a real-life evolution taken from the Graphical Modeling Framework, we
invite submissions to explore ways in which model transformation and migration
tools can be used to migrate models in response to metamodel adaptation.Comment: In Proceedings TTC 2011, arXiv:1111.440
Graphical modeling of stochastic processes driven by correlated errors
We study a class of graphs that represent local independence structures in
stochastic processes allowing for correlated error processes. Several graphs
may encode the same local independencies and we characterize such equivalence
classes of graphs. In the worst case, the number of conditions in our
characterizations grows superpolynomially as a function of the size of the node
set in the graph. We show that deciding Markov equivalence is coNP-complete
which suggests that our characterizations cannot be improved upon
substantially. We prove a global Markov property in the case of a multivariate
Ornstein-Uhlenbeck process which is driven by correlated Brownian motions.Comment: 43 page
One-Component Regular Variation and Graphical Modeling of Extremes
The problem of inferring the distribution of a random vector given that its
norm is large requires modeling a homogeneous limiting density. We suggest an
approach based on graphical models which is suitable for high-dimensional
vectors.
We introduce the notion of one-component regular variation to describe a
function that is regularly varying in its first component. We extend the
representation and Karamata's theorem to one-component regularly varying
functions, probability distributions and densities, and explain why these
results are fundamental in multivariate extreme-value theory. We then
generalize Hammersley-Clifford theorem to relate asymptotic conditional
independence to a factorization of the limiting density, and use it to model
multivariate tails
Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions
Hawkes (1971) introduced a powerful multivariate point process model of
mutually exciting processes to explain causal structure in data. In this paper
it is shown that the Granger causality structure of such processes is fully
encoded in the corresponding link functions of the model. A new nonparametric
estimator of the link functions based on a time-discretized version of the
point process is introduced by using an infinite order autoregression.
Consistency of the new estimator is derived. The estimator is applied to
simulated data and to neural spike train data from the spinal dorsal horn of a
rat.Comment: 20 pages, 4 figure
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