# Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

arXiv: https://arxiv.org/abs/1410.6834 · HTML: https://mlsanity.com/paper/1410.6834/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2014.

## Built on by (most cited first, 6 of 6)

- [Deep Neyman-Scott Processes](https://mlsanity.com/paper/2111.03949/) (2021)
- [Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches](https://mlsanity.com/paper/2501.14291/) (2025)
- [Nonparametric estimation of Hawkes processes with RKHSs](https://mlsanity.com/paper/2411.00621/) (2024)
- [Survival Analysis of the Compressor Station Based on Hawkes Process with Weibull Base Intensity](https://mlsanity.com/paper/2112.13581/) (2021)
- [Exact Bayesian Gaussian Cox Processes Using Random Integral](https://mlsanity.com/paper/2406.19722/) (2024)
- [A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior](https://mlsanity.com/paper/2509.21996/) (2025)

## Abstract (arXiv)

In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or to introducing latent thinning points. Unlike competing models that scale cubically and have a squared memory requirement in the number of data points, our model has a linear complexity and memory requirement. We propose an MCMC sampler and show that our model is faster, more accurate and generates less correlated samples than competing models on both synthetic and real-life data. Finally, we show that our model easily handles data sizes not considered thus far by alternate approaches.