Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields
Latent field models are essential for spatial statistics, but the posterior distribution of the latent field, even with a Gaussian process (GP) prior, is analytically intractable whenever the likelihood is non-Gaussian.
This paper introduces a scalable variational inference framework for spatial statistics, addressing challenges in modeling non-Gaussian spatial fields using sparse autoregressive structures and neural conditioning.
What happened
The article presents a novel variational inference method for spatial statistics, focusing on scalable computation for non-Gaussian spatial fields through autoregressive approximations and neural conditioning techniques.
Why it matters
This approach enables more efficient analysis of large spatial datasets with non-Gaussian properties, which is critical for applications in environmental modeling, geostatistics, and other fields requiring scalable probabilistic inference.
- Variational inference for spatial models benefits from autoregressive structure and neural conditioning
- Amortization reduces computational complexity in probabilistic spatial modeling
- Doubly stochastic optimization enables efficient large-scale inference
The bigger picture
The method advances scalable Bayesian inference for spatial statistics, potentially improving computational efficiency in domains requiring analysis of large non-Gaussian spatial datasets.
The paper contributes a scalable variational inference framework for non-Gaussian spatial fields, combining autoregressive structures, normalizing flows, and neural amortization to address computational challenges in spatial statistics.
Technical details
- Sparse nearest-neighbor autoregressive variational family
- Normalizing-flow conditionals for flexible posterior approximation
- Neural conditioner for amortized variational parameter learning
- Doubly stochastic optimization with reduced ancestor sets
FAQ
What problem does this paper address?
The paper addresses the challenge of intractable posterior distributions in latent field models when dealing with non-Gaussian likelihoods in spatial statistics.
What are the key components of the proposed method?
The method includes sparse nearest-neighbor autoregressive structures, normalizing-flow conditionals, neural amortization of variational parameters, and doubly stochastic optimization with reduced ancestor sets.
Why is scalability important for spatial inference?
Scalability enables efficient analysis of large spatial datasets, which is critical for real-world applications where computational resources and data size pose significant constraints.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - stat.ML on Oct 12, 2026.
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