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About this paper
Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
DCC separates probabilistic programs into cases with fixed sets of variables, performs local inference in each, then combines cases using estimated marginal probabilities. It balances effort between promising and uncertain cases. In function induction, across 15 runs with one million samples per method, DCC achieved mean test LPPD of −28.560 versus RMH’s −32.693, supporting better predictive fit in that experiment, not universal superiority.
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