Results 21 to 30 of about 606,212 (231)
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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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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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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Network Inference from Co-Occurrences [PDF]
, 2006 The recovery of network structure from experimental data is a basic and fundamental problem. Unfortunately, experimental data often do not directly reveal structure due to inherent limitations such as imprecision in timing or other observation mechanisms.
Figueiredo, Mario +2 more
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Frontiers in Applied Mathematics and Statistics, 2022 In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons.
Thierry Nieus +5 more
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Granger-Causality Inference of the Existence of Unobserved Important Components in Network Analysis
Entropy, 2021 Detecting causal interrelationships in multivariate systems, in terms of the Granger-causality concept, is of major interest for applications in many fields. Analyzing all the relevant components of a system is almost impossible, which contrasts with the
Heba Elsegai
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FINET: Fast Inferring NETwork [PDF]
BMC Research Notes, 2019 Abstract Objectives Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive
Anyou Wang, Rong Hai
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Decoupling approximation robustly reconstructs directed dynamical networks
New Journal of Physics, 2018 Methods for reconstructing the topology of complex networks from time-resolved observations of node dynamics are gaining relevance across scientific disciplines.
Nikola Simidjievski +5 more
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