Particle Gibbs with ancestor sampling for probabilistic programs
Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sam...
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Journal of Machine Learning Research
2015
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