15,825 research outputs found
One Kind of Multiple Dimensional Markovian BSDEs with Stochastic Linear Growth Generators
In this article, we deal with a multiple dimensional coupled Markovian BSDEs
system with stochastic linear growth generators with respect to volatility
processes. An existence result is provided by using approximation techniques.Comment: arXiv admin note: text overlap with arXiv:1412.121
Logic motif of combinatorial control in transcriptional networks
Combinatorial control is prevalent in transcriptional regulatory networks. However, whether there are specific logic patterns over- or under-represented in real networks remains uninvestigated. Using a theoretic model and _in-silico_ simulations, we systematically study how the relative abundance of distinct regulatory logic patterns influences the network’s global dynamics. We find that global dynamic characteristics are sensitive to several specific logic patterns regardless of the detailed network topology. We show it is possible to infer logic motifs based on the sensitivity profile and the biological interpretations of these global characteristics
Learning Loosely Connected Markov Random Fields
We consider the structure learning problem for graphical models that we call
loosely connected Markov random fields, in which the number of short paths
between any pair of nodes is small, and present a new conditional independence
test based algorithm for learning the underlying graph structure. The novel
maximization step in our algorithm ensures that the true edges are detected
correctly even when there are short cycles in the graph. The number of samples
required by our algorithm is C*log p, where p is the size of the graph and the
constant C depends on the parameters of the model. We show that several
previously studied models are examples of loosely connected Markov random
fields, and our algorithm achieves the same or lower computational complexity
than the previously designed algorithms for individual cases. We also get new
results for more general graphical models, in particular, our algorithm learns
general Ising models on the Erdos-Renyi random graph G(p, c/p) correctly with
running time O(np^5).Comment: 45 pages, minor revisio
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