Bayesian Inference
Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
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Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Neural Information Processing Systems
Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.
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The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Neural Information Processing Systems
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Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Neural Information Processing Systems
However, it is known that even with infinite i.i.d.
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Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
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Neural Information Processing Systems http://nips.cc/
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Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
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Neural Information Processing Systems http://nips.cc/
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