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Stochastic generative model
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using directed
acyclic graph
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(e.g. Bayes Net)
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Generation
from model is easy
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Inference
is generally hard
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Learning
is easy after inference
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Energy-based models that
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associate an
energy with each
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joint
configuration
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Generation
from model is hard
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Inference
is generally hard
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Learning
requires a negative
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phase
that is even harder
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than inference
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