52,746 research outputs found
Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-varying
smoothness in the mean and covariance process. In particular, there may be
certain time intervals exhibiting rapid changes and others in which changes are
slow. If such time-varying smoothness is not accounted for, one can obtain
misleading inferences and predictions, with over-smoothing across erratic time
intervals and under-smoothing across times exhibiting slow variation. This can
lead to mis-calibration of predictive intervals, which can be substantially too
narrow or wide depending on the time. We propose a locally adaptive factor
process for characterizing multivariate mean-covariance changes in continuous
time, allowing locally varying smoothness in both the mean and covariance
matrix. This process is constructed utilizing latent dictionary functions
evolving in time through nested Gaussian processes and linearly related to the
observed data with a sparse mapping. Using a differential equation
representation, we bypass usual computational bottlenecks in obtaining MCMC and
online algorithms for approximate Bayesian inference. The performance is
assessed in simulations and illustrated in a financial application
A generative model for sparse, evolving digraphs
Generating graphs that are similar to real ones is an open problem, while the
similarity notion is quite elusive and hard to formalize. In this paper, we
focus on sparse digraphs and propose SDG, an algorithm that aims at generating
graphs similar to real ones. Since real graphs are evolving and this evolution
is important to study in order to understand the underlying dynamical system,
we tackle the problem of generating series of graphs. We propose SEDGE, an
algorithm meant to generate series of graphs similar to a real series. SEDGE is
an extension of SDG. We consider graphs that are representations of software
programs and show experimentally that our approach outperforms other existing
approaches. Experiments show the performance of both algorithms
Evolution of Neural Networks for Helicopter Control: Why Modularity Matters
The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so
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