= Stochastic computation graph
A <stochastic computation graph> is a <Directed acyclic graph> combining deterministic transformations, random choices and costs. The changing <probabilities> of random choices contribute <score-function gradient estimators>, while differentiable deterministic paths contribute <pathwise gradients>. This accounts for both sources of a parameter's effect on an expected cost.<Gradient Estimation Using Stochastic Computation Graphs by John Schulman and others>[Sections 2–3]{ref}
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