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Journal Article

Latent network models to account for noisy, multiply reported social network data

MPS-Authors
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De Bacco,  Caterina       
Max Planck Research Group Physics for Inference and Optimization, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Contisciani,  Martina       
Max Planck Research Group Physics for Inference and Optimization, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Safdari,  Hadiseh       
Max Planck Research Group Physics for Inference and Optimization, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Baptista,  Diego
Max Planck Research Group Physics for Inference and Optimization, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1093/jrsssa/qnac004
(Publisher version)

https://doi.org/10.48550/arXiv.2112.11396
(Preprint)

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Citation

De Bacco, C., Contisciani, M., Cardoso-Silva, J., Safdari, H., Borges, G. L., Baptista, D., et al. (2023). Latent network models to account for noisy, multiply reported social network data. Journal of the Royal Statistical Society - Series A: Statistics in Society, 186(3), 355-375. doi:10.1093/jrsssa/qnac004.


Cite as: https://hdl.handle.net/21.11116/0000-000F-E2F3-F
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