Generating Efficient MCMC Kernels from Probabilistic Programs

  title={Generating Efficient MCMC Kernels from Probabilistic Programs},
  author={Lingfeng Yang and Pat Hanrahan and Noah D. Goodman},
Universal probabilistic programming languages (such as Church [6]) trade performance for abstraction: any model can be represented compactly as an arbitrary stochastic computation, but costly online analyses are required for inference. We present a technique that recovers hand-coded levels of performance from a universal probabilistic language, for the Metropolis-Hastings (MH) MCMC inference algorithm. It takes a Church program as input and traces its execution to remove computation overhead… CONTINUE READING


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