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Learning Probabilistic Programs
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We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficiently powerful to enable successful inference of this kind in nontrivial domains. We also introduce a new notion of probabilistic program compilation and show how the same machinery might be used in the future to compile probabilistic programs for efficient reusable predictive inference.
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Cited by 1 Pith paper
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Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Bayesian synthesis formulates automatic construction of probabilistic programs in PCFG-specified DSLs with soundness conditions, enabling structure analysis and prediction that outperforms baselines on real datasets.
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