Groundwater quality assessment using data clustering based on hybrid Bayesian networks
作者： Pedro A. Aguilera , Antonio Fernández , Rosa F. Ropero , Luís Molina
DOI: 10.1007/S00477-012-0676-8
关键词:
摘要: Bayesian networks (BNs) have become a standard in the field of Artificial Intelligence as a means of dealing with uncertainty and risk modelling. In recent years, there has been …
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Kristian G. Olesen, Finn V. Jensen, Steffen L. Lauritzen, Bayesian updating in causal probabilistic networks by local computations Computational Statistics Quarterly. ,vol. 4, pp. 269- 282 ,(1990)
Barry R. Cobb, Rafael Rumí, Antonio Salmerón, Bayesian Network Models with Discrete and Continuous Variables Springer Berlin Heidelberg. pp. 81- 102 ,(2007) , 10.1007/978-3-540-68996-6_4
Rafael Rumí, Antonio Salmerón, José A. Gámez, Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials probabilistic graphical models. pp. 123- 130 ,(2006)
Clark N. Glymour, Peter Spirtes, Richard Scheines, Causation, prediction, and search ,(1993)
Steffen L. Lauritzen, Frank Jensen, Stable local computation with conditional Gaussian distributions Statistics and Computing. ,vol. 11, pp. 191- 203 ,(2001) , 10.1023/A:1008935617754
Serafín Moral, Rafael Rumi, Antonio Salmerón, Mixtures of Truncated Exponentials in Hybrid Bayesian Networks european conference on symbolic and quantitative approaches to reasoning and uncertainty. pp. 156- 167 ,(2001) , 10.1007/3-540-44652-4_15
Serafín Moral, Rafael Rumí, Antonio Salmerón, Approximating Conditional MTE Distributions by Means of Mixed Trees european conference on symbolic and quantitative approaches to reasoning and uncertainty. pp. 173- 183 ,(2003) , 10.1007/978-3-540-45062-7_14
Rafael Rumí, Serafín Moral, Antonio Salmerón, Estimating mixtures of truncated exponentials from data probabilistic graphical models. ,(2002)
Finn B. Jensen, Thomas Graven-Nielsen, Bayesian networks and decision graphs ,(2001)
Nir Friedman, Dan Geiger, Moises Goldszmidt, Bayesian Network Classifiers Machine Learning. ,vol. 29, pp. 131- 163 ,(1997) , 10.1023/A:1007465528199