mlsanityData to 7 Oct 2026

Diffusion models for probabilistic programming

arXiv 2311.00474 ↗First posted 1 Nov 2023cs.LG stat.MLcited by 1

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 18 of its 25 references that are in the map. First posted on arXiv 1 Nov 2023.

Builds on

18 of 25 references.

References · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2015Variational Inference with Normalizing Flowscited by 2,161
  8. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  9. 2016Variational Inference: A Review for Statisticianscited by 1,466
  10. 2021Variational Diffusion Modelscited by 1,145
  11. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  12. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 594
6 more
  1. 2017A Conceptual Introduction to Hamiltonian Monte Carlocited by 207
  2. 2019Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyrocited by 187
  3. 2016Automatic Differentiation Variational Inferencecited by 159
  4. 2015Hierarchical Variational Modelscited by 89
  5. 2018An Introduction to Probabilistic Programmingcited by 62
  6. 2020Variational Inference with Continuously-Indexed Normalizing Flowscited by 14

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Causal Posterior Estimationcited by 1

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  1. 2025A Frequentist Statistical Introduction to Variational Inference, Autoencoders, and Diffusion Models
  2. 2026Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
  3. 2026Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming
  4. 2016Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
  5. 2026CAI-DLLM: Convergence Aware Inference for Diffusion Language Models
  6. 2026Breaking the Factorization Barrier in Diffusion Language Models
  7. 2023Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
  8. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm