Bayesian Artificial Intelligence

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Learning Neural Causal Models from Unknown Interventions (2019, methodology): “Causal structure learning using SCMs has been attempted in several disciplines including biology [2, 3], weather forecasting [4] and medicine [5, 6].”
Bayesian models of cognition (2008, methodology): “For the generative models represented by Figure 2a or 2b, we have assumed that all variables except θ are observed in each sample from the model, or each data point.”
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks (2006, background): “Conditional independency means that when the Markov blanket of a certain variable x is known, adding knowledge of other variables leaves the probability of x unchanged (Korb and Nicholson (2004)).”