Diffusion models for probabilistic programming

• URL: http://arxiv.org/abs/2311.00474v2
• Date: Tue, 21 Nov 2023 20:16:57 GMT
• Title: Diffusion models for probabilistic programming
• Authors: Simon Dirmeier and Fernando Perez-Cruz
• Abstract summary: Diffusion Model Variational Inference (DMVI) is a novel method for automated approximate inference in probabilistic programming languages (PPLs) DMVI is easy to implement, allows hassle-free inference in PPLs without the drawbacks of, e.g., variational inference using normalizing flows, and does not make any constraints on the underlying neural network model.
• Score: 56.47577824219207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion models as variational approximations to the true posterior distribution by deriving a novel bound to the marginal likelihood objective used in Bayesian modelling. DMVI is easy to implement, allows hassle-free inference in PPLs without the drawbacks of, e.g., variational inference using normalizing flows, and does not make any constraints on the underlying neural network model. We evaluate DMVI on a set of common Bayesian models and show that its posterior inferences are in general more accurate than those of contemporary methods used in PPLs while having a similar computational cost and requiring less manual tuning.

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