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arXiv Machine Learning By Paul F. V. Wiemann, Matthias Katzfuss

Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields

Read the original on arXiv Machine Learning →

The paper introduces a scalable variational inference framework for latent field models with non‑Gaussian likelihoods. It mirrors the sparse nearest‑neighbor autoregressive structure of the Gaussian process prior in the variational family, employs normalizing‑flow conditionals for expressiveness, and amortizes all variational parameters through a single shared neural conditioner. The method achieves polylogarithmic per‑iteration complexity via doubly stochastic optimization with reduced ancestor sets.

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