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We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks
Jian Cheng and Marek J. Druzdzel · 2000
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Recognition networks for approximate inference in bn20 networks
Quaid Morris · 2001
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Annealed importance sampling
Radford M Neal · 2001
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Any reasonable cost function can be used for a posteriori probability approximation
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Neural adaptive sequential monte carlo
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Inference networks for sequential Monte Carlo in graphical models
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Using synthetic data to train neural networks is model-based reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, and Frank Wood · 2017
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