Recently introduced prior-encoding deep generative models (e.g., PriorVAE, VAE, and PriorCVAE) have emerged as powerful tools for scalable Bayesian inference by emulating complex stochastic processes like Gaussian processes (GPs). However, these methods remain largely a proof-of-concept and inaccessible to practitioners. We propose DeepRV, a lightweight, decoder-only approach that accelerates training, and enhances real-world applicability in comparison to current VAE-based prior encoding approaches. Leveraging probabilistic programming frameworks (e.g., NumPyro) for inference, DeepRV achieves significant speedups while also improving the quality of parameter inference, closely matching full MCMC sampling. We showcase its effectiveness in process emulation and spatial analysis of the UK using simulated data, gender-wise cancer mortality rates for individuals under 50, and HIV prevalence in Zimbabwe. To bridge the gap between theory and practice, we provide a user-friendly API, enabling scalable and efficient Bayesian inference.
View on arXiv@article{navott2025_2503.21473,
title={ DeepRV: pre-trained spatial priors for accelerated disease mapping },
author={ Jhonathan Navott and Daniel Jenson and Seth Flaxman and Elizaveta Semenova },
journal={arXiv preprint arXiv:2503.21473},
year={ 2025 }
}