Martingale Posterior Cox Processes: GPU-friendly Bayesian intensity estimation at a fraction of the cost
chaumian · x · 2026-10-23
A new arXiv paper (2610.24787) by Joshua Rooijakkers and Edwin Fong tackles Bayesian intensity estimation in Cox processes.
- Existing methods rely on expensive MCMC or approximate inference, often defining priors over indirect latent structures that are hard to interpret.
- MPCP recasts Bayesian intensity estimation as inference over unobserved events given the observed event history, avoiding complex priors while retaining nonparametric flexibility.
- The authors propose a scalable, GPU-friendly copula-based predictive update with theoretical convergence guarantees for the predictive resampling algorithm.
- Empirically, MPCP matches or beats competing Bayesian approaches on simulations and real data at a fraction of the computational cost.
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