mlsanityData to 7 Oct 2026

PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference

arXiv 2510.13763 ↗First posted 15 Oct 2025stat.ML cs.LGcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging modern generative methods like diffusion models to map observed data to model parameters or future predictions. These approaches yield posterior or posterior-predictive samples for new datasets without requiring further simulator calls after training on simulated parameter-data pairs. However, their applicability is often limited by the prior distribution(s) used to generate model parameters during this training phase. To overcome this constraint, we introduce PriorGuide, a technique specifically designed for diffusion-based amortized inference methods. PriorGuide leverages a novel guidance approximation that enables flexible adaptation of the trained diffusion model to new priors at test time, crucially without costly retraining. This allows users to readily incorporate updated information or expert knowledge post-training, enhancing the versatility of pre-trained inference models.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 59 of its 85 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

59 of 85 references.

Closest ancestors · 14

Same direction, newest first

  1. 2025Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts4 Mar
  2. 2025Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo18 Feb
  3. 2025Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo10 Feb
  4. 2024Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs15 Oct
  5. 2023On the Posterior Distribution in Denoising: Application to Uncertainty Quantification24 Sep
  6. 2023User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems13 Jun
  7. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC22 Feb
  8. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models10 Oct
  9. 2022Classifier-Free Diffusion Guidance26 Jul
  10. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  11. 2022Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models17 Jan
  12. 2021Diffusion Models Beat GANs on Image Synthesis11 May
2 more
  1. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  2. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 45

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  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2022Flow Matching for Generative Modelingcited by 5,426
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  5. 2022Video Diffusion Modelscited by 2,280
  6. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,928
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  11. 2018Conditional Neural Processescited by 421
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33 more
  1. 2016Revisiting Classifier Two-Sample Testscited by 258
  2. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  3. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
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  8. 2018Validating Bayesian Inference Algorithms with Simulation-Based Calibrationcited by 118
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  13. 2024Learning Diffusion Priors from Observations by Expectation Maximizationcited by 60
  14. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
  15. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  16. 2020Bayesian Workflowcited by 47
  17. 2022Compositional Score Modeling for Simulation-based Inferencecited by 46
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  26. 2024Diffusion posterior sampling for simulation-based inference in tall data settingscited by 17
  27. 2025Amortized In-Context Bayesian Posterior Estimationcited by 13
  28. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Minimum Distance Summaries for Robust Neural Posterior Estimationcited by 3
  2. 2026MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidancecited by 0
  3. 2026Guidance for Prior Change via Density Ratio Estimationcited by 0

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