Amortized Variational Inference of Latent Intensities from Replicated Point Patterns
Bayesian inference for latent point process intensities often relies on per-dataset sampling or optimization, making repeated inference computationally expensive. We propose a neural-network-based amortized variational framework that learns a reusable inference rule mapping a variable number of replicated point patterns to a distribution over their shared latent intensity. Trained from event times using the point process likelihood, the model learns an amortized inference rule without ground-truth intensity supervision. At test time, it performs inference of the latent distribution in a forward pass. On synthetic data, additional realizations improve intensity recovery and yield narrower ...
| Authors | Wenqing Liu, Xenia Miscouridou, Déborah Sulem |
|---|---|
| Primary category | stat.ML |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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