Lifted Inference: Normalizing Loops by Evaluation

Free registration required

Executive Summary

Many loops in probabilistic inference map almost every individual in their domain to the same result. Running such loops symbolically takes time sub-linear in the domain size. Using normalization by evaluation with first-class delimited continuations, the authors lift inference procedures to reap this speed-up without interpretive overhead. To express nested loops, they use multiple control delimiters for meta-circular interpretation. To express loops over a power-set domain, they convert nested loops over a subset to unnested loops.

  • Format: PDF
  • Size: 188.23 KB