Causal Randomized Policy
An analytical framework in parallel sampling theory that requires each sampling decision to be based solely on historically available information (causality), while permitting the scheduling policy itself to be randomized. This framework excludes adaptive strategies that exploit future information and serves as the standard model for analyzing round complexity in parallel sampling.
Timeline (last 90 days)
In the causal randomized strategy framework, the decision of which coordinates to sample in round t can only depend on information already generated in the previous t-1 rounds and cannot foresee future randomness.
The doubly logarithmic expected cost is possible because the strategy allows full n-round sampling to be executed with vanishingly small probability.
All Facts (2)
In the causal randomized strategy framework, the decision of which coordinates to sample in round t can only depend on information already generated in the previous t-1 rounds and cannot foresee future randomness.
50%UnverifiedThe doubly logarithmic expected cost is possible because the strategy allows full n-round sampling to be executed with vanishingly small probability.
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