Results 11 to 20 of about 606,212 (231)
Network Inference with Hidden Units
Mathematical Biosciences and Engineering, 2013 We derive learning rules for finding the connections between units in stochastic dynamical networks from the recorded history of a ``visible'' subset of the units. We consider two models. In both of them, the visible units are binary and stochastic.
Hertz, John, Tyrcha, Joanna
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Network Inference with the Lasso
Multivariate Behavioral Research, 2022 Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since Lasso estimation is often used to obtain edges in a network, and the underlying distribution of Lasso estimates is discontinuous and has probability one at zero when the estimate is zero,
Lourens Waldorp, Jonas Haslbeck
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Network neutrality inference [PDF]
ACM SIGCOMM Computer Communication Review, 2014 When can we reason about the neutrality of a network based on external observations? We prove conditions under which it is possible to (a) detect neutrality violations and (b) localize them to specific links, based on external observations. Our insight is that, when we make external observations from different vantage points, these will most likely be ...
Zhang Zhiyong +2 more
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Inferring cellular networks – a review [PDF]
BMC Bioinformatics, 2007 In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical ...
Florian Markowetz, Rainer Spang
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Inferring networks of diffusion and influence [PDF]
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010 Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult.
Manuel Gomez-Rodriguez +2 more
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Trace complexity of network inference [PDF]
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013 25 pages, preliminary version appeared in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013)
Bruno Abrahao +3 more
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Network Cluster‐Robust Inference [PDF]
Econometrica, 2021 Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data,
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Inference of a Boolean Network From Causal Logic Implications
Frontiers in Genetics, 2022 Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model ...
Parul Maheshwari +3 more
doaj +1 more source
Quantum inference on Bayesian networks [PDF]
Physical Review A, 2014 Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on $n$ variables with at most $m$ parents per node in
Low, Guang Hao +2 more
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Iterative procedure for network inference [PDF]
Communications in Nonlinear Science and Numerical Simulation, 2020 When a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree distribution of the true underlying network is analytically reconstructed using an iterative procedure.
Gloria Cecchini, Björn Schelter
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