Bayesian Artificial Intelligence

By D. Zelterman. Technometrics, 2005

729 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bdaf90fca684923edbaa15af0530f63b6974e345

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In Artificial Intelligence

Citations in the corpus

Explaining inferences in Bayesian networks (2008, background): “, there cannot be “backdoors” in a BN [19].”
Regime Aware Learning (2016, background): “For a full treatment of BNs please see Pearl (1988); Korb and Nicholson (2011); Jensen and Nielsen (2007).”
Learning Bayesian Networks with the bnlearn R Package (2009, methodology): “Bayesian networks Bayesian networks are graphical models where nodes represent random variables (the two terms are used interchangeably in this article) and arcs represent probabilistic dependencies between them (Korb and Nicholson 2004).”
Prediction of Slope Stability using Naive Bayes Classifier (2018, methodology): “It is infeasible to specify an accurate density function if the data are limited (Korb and Nicholson, 2004).”
An Evolutionary-Network Model Reveals Stratified Interactions in the V3 Loop of the HIV-1 Envelope (2007, background): “more parsimonious and informative representation of biological causation [43].”
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment (2007, methodology): “When data are available, several measures can be used to evaluate the Bn, including predictive accuracy, expected value computations and information reward (Korb and Nicholson, 2004).”
Causal Pluralism versus Epistemic Causality (2006, background): “There are a whole host of algorithms for finding minimal causal graphs that satisfy the Causal Markov Condition and some set of causal constraints (Korb and Nicholson, 2003, Appendix B).”