Probabilistic Abductive Logic Programming using Dirichlet Priors
نویسندگان
چکیده
Probabilistic logic programming has traditionally focused on languages where probabilities or weights are specified or inferred directly, rather than through Bayesian priors. To address this limitation, we propose a probabilistic logic programming language that bridges the gap between logical and probabilistic inference in categorical models with Dirichlet priors. The language is described in terms of its general plate model, syntax, semantics and the relation between the three. A prototype implementation is evaluated on two case studies: latent Dirichlet allocation (LDA) on synthetic data, where we compare it with collapsed Gibbs sampling, and repeated insertion model (RIM) on real data. Universal probabilistic programming is not always scalable beyond toy examples on some models. However, our promising results show that the inference yields similar results to state-of-the-art solutions reported in the literature, produced with model-specific implementations.
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