Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters

Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underl...

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Detalles Bibliográficos
Autores principales: Di, Ruohai, Wang, Peng, He, Chuchao, Guo, Zhigao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534477/
https://www.ncbi.nlm.nih.gov/pubmed/34682007
http://dx.doi.org/10.3390/e23101283

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534477/
https://www.ncbi.nlm.nih.gov/pubmed/34682007
http://dx.doi.org/10.3390/e23101283

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