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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. Existing approximations are either too slow or too restrictive for large non-Gau
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