mlsanityData to 14 Oct 2026

Deep Neyman-Scott Processes

arXiv 2111.03949 ↗First posted 6 Nov 2021stat.ML cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

A Neyman-Scott process is a special case of a Cox process. The latent and observable stochastic processes are both Poisson processes. We consider a deep Neyman-Scott process in this paper, for which the building components of a network are all Poisson processes. We develop an efficient posterior sampling via Markov chain Monte Carlo and use it for likelihood-based inference. Our method opens up room for the inference in sophisticated hierarchical point processes. We show in the experiments that more hidden Poisson processes brings better performance for likelihood fitting and events types prediction. We also compare our method with state-of-the-art models for temporal real-world datasets and demonstrate competitive abilities for both data fitting and prediction, using far fewer parameters.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 64 references that are in the map. First posted on arXiv 6 Nov 2021.

Builds on

14 of 64 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Transformer Hawkes Processcited by 235
  3. 2019Intensity-Free Learning of Temporal Point Processescited by 140
  4. 2019Fully Neural Network based Model for General Temporal Point Processescited by 122
  5. 2015SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularitycited by 76
  6. 2019A Variational Auto-Encoder Model for Stochastic Point Processescited by 30
  7. 2014Variational Inference for Gaussian Process Modulated Poisson Processescited by 26
  8. 2018Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processescited by 17
  9. 2018Efficient Non-parametric Bayesian Hawkes Processescited by 15
  10. 2020Point process models for sequence detection in high-dimensional neural spike trainscited by 10
  11. 2019Structured Variational Inference in Continuous Cox Process Modelscited by 8
  12. 2014Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processescited by 6
2 more
  1. 2014Efficient Bayesian Nonparametric Modelling of Structured Point Processescited by 5
  2. 2019Efficient EM-Variational Inference for Hawkes Processcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

6 papers.

  1. 2023Add and Thin: Diffusion for Temporal Point Processescited by 23
  2. 2024Unlocking Point Processes through Point Set Diffusioncited by 14
  3. 2022Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Modelscited by 3
  4. 2023Variational Inference for Neyman-Scott Processescited by 0
  5. 2024On the Efficient Marginalization of Probabilistic Sequence Modelscited by 0
  6. 2026Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinalitycited by 0