Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909849644695552 |
|---|---|
| author | Janati, Yazid Durmus, Alain Olsson, Jimmy Moulines, Eric |
| author_facet | Janati, Yazid Durmus, Alain Olsson, Jimmy Moulines, Eric |
| contents | Diffusion models enable the synthesis of highly accurate samples from complex distributions and have become foundational in generative modeling. Recently, they have demonstrated significant potential for solving Bayesian inverse problems by serving as priors. This review offers a comprehensive overview of current methods that leverage \emph{pre-trained} diffusion models alongside Monte Carlo methods to address Bayesian inverse problems without requiring additional training. We show that these methods primarily employ a \emph{twisting} mechanism for the intermediate distributions within the diffusion process, guiding the simulations toward the posterior distribution. We describe how various Monte Carlo methods are then used to aid in sampling from these twisted distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey Janati, Yazid Durmus, Alain Olsson, Jimmy Moulines, Eric Machine Learning Diffusion models enable the synthesis of highly accurate samples from complex distributions and have become foundational in generative modeling. Recently, they have demonstrated significant potential for solving Bayesian inverse problems by serving as priors. This review offers a comprehensive overview of current methods that leverage \emph{pre-trained} diffusion models alongside Monte Carlo methods to address Bayesian inverse problems without requiring additional training. We show that these methods primarily employ a \emph{twisting} mechanism for the intermediate distributions within the diffusion process, guiding the simulations toward the posterior distribution. We describe how various Monte Carlo methods are then used to aid in sampling from these twisted distributions. |
| title | Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.14114 |