Deep Probabilistic Spatial Modeling for Multivariate Mixed-Type Responses

TL;DR

This paper introduces MultiDeepGP, a scalable deep probabilistic model for joint analysis of multivariate mixed-type spatial data, capturing complex dependencies and providing reliable uncertainty quantification.

Contribution

It develops a novel framework combining deep latent representations with a shared spatial component for coherent joint modeling of mixed outcomes.

Findings

Accurately predicts multivariate mixed spatial data

Provides reliable uncertainty quantification

Demonstrates effectiveness on environmental health data

Abstract

Many scientific applications involve mixed spatially indexed outcomes of heterogeneous types that are driven by shared latent mechanisms. Modeling such data is challenging due to complex, nonlinear, and potentially nonstationary spatial dependence, as well as the need for coherent joint inference across mixed outcome distributions. Existing multivariate mixed outcome models often rely on restrictive linear assumptions, while recent deep learning approaches emphasize predictive flexibility but typically lack coherent joint modeling and uncertainty quantification for spatial data. We develop MultiDeepGP, a scalable and statistically principled framework for joint modeling of multivariate mixed outcomes in spatial settings. The proposed approach introduces a shared latent spatial component that governs cross-outcome dependence while allowing outcome-specific distributions. Spatial dependence and nonlinear structure are captured through a deep latent representation, and uncertainty quantification is enabled via an efficient Monte Carlo-based inference strategy. This construction balances modeling flexibility with probabilistic interpretability and computational feasibility. The proposed method is evaluated through simulation studies designed to reflect key challenges in mixed outcome spatial modeling, as well as an application to georeferenced environmental and public health data from the African Great Lakes region. The results demonstrate that the proposed framework provides accurate joint prediction and reliable uncertainty quantification in complex spatial settings.