Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2024

Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms

Francesca R. Crucinio
  • Fonction : Auteur
Pablo Jiménez
  • Fonction : Auteur
Gareth O. Roberts
  • Fonction : Auteur

Résumé

We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and non-differentiable targets. These methods have been shown to be stable for a wide class of targets, making them a valuable alternative to Metropolis-adjusted Langevin algorithms (MALA); and have found wide application in imaging contexts. The wider stability properties are obtained by building the Moreau-Yoshida envelope for the target of interest, which depends on a parameter $\lambda$. In this work, we investigate the optimal scaling problem for this class of algorithms, which encompasses MALA, and provide practical guidelines for the implementation of these methods.

Dates et versions

hal-04396502 , version 1 (16-01-2024)

Identifiants

Citer

Francesca R. Crucinio, Alain Durmus, Pablo Jiménez, Gareth O. Roberts. Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms. 2024. ⟨hal-04396502⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

More