Results 21 to 30 of about 5,161,693 (301)
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
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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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Granger causality vs. dynamic Bayesian network inference: a comparative study [PDF]
, 2009 Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data.
Denby Katherine J +9 more
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Quantifying the ‘end of history’ through a Bayesian Markov-chain approach
Royal Society Open Science, 2022 Political regimes have been changing throughout human history. After the apparent triumph of liberal democracies at the end of the twentieth century, Francis Fukuyama and others have been arguing that humankind is approaching an ‘end of history’ (EoH) in
Florian Klimm
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Inference and Validation of Networks [PDF]
, 2009 We develop a statistical methodology to validate the result of network inference algorithms, based on principles of statistical testing and machine learning. The comparison of results with reference networks, by means of similarity measures and null models, allows us to measure the significance of results, as well as their predictive power.
Ilias N. Flaounas +3 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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Entropy, 2016 Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear.
Yanzhu Hu, Huiyang Zhao, Xinbo Ai
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Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents
Entropy, 2022 Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations ...
Zhongqi Cai +2 more
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Fourier Bayesian networks: A novel approach for network structure inference with application to brain connectivity studies from magnetoencephalographic recordings [PDF]
, 2012 This thesis proposes a novel approach for connectivity studies in Electrophysiology and Neuroimaging based on Bayesian Network (BN) analysis in the Fourier domain that is named Fourier Bayesian Networks (FBNs). FBNs use the complex information available
Peraza Rodriguez, Luis Ramon
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In-Network Learning: Distributed Training and Inference in Networks
Entropy, 2023 In this paper, we study distributed inference and learning over networks which can be modeled by a directed graph. A subset of the nodes observes different features, which are all relevant/required for the inference task that needs to be performed at some distant end (fusion) node.
Moldoveanu, Matei, Zaidi, Abdellatif
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