PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation.
J R Soc Interface
; 19(191): 20220094, 2022 06.
Article
de En
| MEDLINE
| ID: mdl-35673858
ABSTRACT
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Analyse en petite superficie
/
Analyse spatiale
Type d'étude:
Prognostic_studies
Limites:
Humans
Langue:
En
Journal:
J R Soc Interface
Année:
2022
Type de document:
Article
Pays d'affiliation:
Royaume-Uni