Results 31 to 40 of about 654,785 (260)
Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]
Royal Society Open Science, 2019 Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel +2 more
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Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
Journal of Statistical Software, 2023 The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior ...
Polina Suter +3 more
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Multiple conformations facilitate PilT function in the type IV pilus
Nature Communications, 2019 Bacterial type IV pilus-like systems catalyse the formation of pilin fibres but it is unknown how they are powered. Here, the authors present crystal and cryo-EM structures of the hexameric motor ATPases PilB and PilT from Type IVa Pilus that reveal ...
Matthew McCallum +6 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Machine-learning mathematical structures
International Journal of Data Science in the Mathematical Sciences, 2022 We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, we present a ...
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Journal of Statistical Software, 2017 It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world ...
Marco Scutari
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IEEE Access, 2022 Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search & score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is ...
Kunhua Zhong +3 more
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Social structure learning in human anterior insula
eLife, 2020 Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and ...
Tatiana Lau +2 more
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Behavioural Brain Research, 2010 'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'
Braun, Daniel A. +2 more
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