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Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields
arXiv stat.ML0 views
arXiv:2610.13656v1 Announce Type: new Abstract: 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. Existing approximations are either too slow or too restrictive for large non-Gaussian datasets. We present a scalable variational inference framework that mirrors the sparse nearest-neighbor autoregressive structure of the prior in the variational family, uses normalizing-flow conditionals for expressiveness, and amortizes all variational parameters via a single shared neural conditioner. Doubly stochastic optimization with reduced ancestor sets achieves polylogarithmic complexity per iteration.
Source：https://arxiv.org/abs/2610.13656PreviousA Critical Look at Power Sampling when Test Time Sharpening LLMsNextNot All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels
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