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Efficient Variational Inference for Log-Gaussian Cox Processes via Voronoi Tessellation

NeurIPS 2026 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Sparse variational Gaussian processes

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Proposes VoGCAM, a variational inference method for Log-Gaussian Cox Processes that approximates the intractable LGCP integral with a Voronoi tessellation and fits the model via Gaussian coordinate ascent.

Problem

Log-Gaussian Cox Processes for spatial point patterns have high computational cost and intractable likelihoods due to the integral term.

Approach

Approximates the intractable LGCP likelihood integral using a flexible Voronoi tessellation that incorporates observed points, then applies variational Gaussian coordinate ascent for efficient inference.

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