4,781 research outputs found
Partial norms and the convergence of general products of matrices
Motivated by the theory of inhomogeneous Markov chains, we determine a
sufficient condition for the convergence to 0 of a general product formed from
a sequence of real or complex matrices. When the matrices have a common
invariant subspace , we give a sufficient condition for the convergence to 0
on of a general product. Our result is applied to obtain a condition for
the weak ergodicity of an inhomogeneous Markov chain. We compare various types
of contractions which may be defined for a single matrix, such as
paracontraction, --contraction, and --contraction, where is an
invariant subspace of the matrix
The notion of -weak dependence and its applications to bootstrapping time series
We give an introduction to a notion of weak dependence which is more general
than mixing and allows to treat for example processes driven by discrete
innovations as they appear with time series bootstrap. As a typical example, we
analyze autoregressive processes and their bootstrap analogues in detail and
show how weak dependence can be easily derived from a contraction property of
the process. Furthermore, we provide an overview of classes of processes
possessing the property of weak dependence and describe important probabilistic
results under such an assumption.Comment: Published in at http://dx.doi.org/10.1214/06-PS086 the Probability
Surveys (http://www.i-journals.org/ps/) by the Institute of Mathematical
Statistics (http://www.imstat.org
NEFI: Network Extraction From Images
Networks and network-like structures are amongst the central building blocks
of many technological and biological systems. Given a mathematical graph
representation of a network, methods from graph theory enable a precise
investigation of its properties. Software for the analysis of graphs is widely
available and has been applied to graphs describing large scale networks such
as social networks, protein-interaction networks, etc. In these applications,
graph acquisition, i.e., the extraction of a mathematical graph from a network,
is relatively simple. However, for many network-like structures, e.g. leaf
venations, slime molds and mud cracks, data collection relies on images where
graph extraction requires domain-specific solutions or even manual. Here we
introduce Network Extraction From Images, NEFI, a software tool that
automatically extracts accurate graphs from images of a wide range of networks
originating in various domains. While there is previous work on graph
extraction from images, theoretical results are fully accessible only to an
expert audience and ready-to-use implementations for non-experts are rarely
available or insufficiently documented. NEFI provides a novel platform allowing
practitioners from many disciplines to easily extract graph representations
from images by supplying flexible tools from image processing, computer vision
and graph theory bundled in a convenient package. Thus, NEFI constitutes a
scalable alternative to tedious and error-prone manual graph extraction and
special purpose tools. We anticipate NEFI to enable the collection of larger
datasets by reducing the time spent on graph extraction. The analysis of these
new datasets may open up the possibility to gain new insights into the
structure and function of various types of networks. NEFI is open source and
available http://nefi.mpi-inf.mpg.de
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