# A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior

arXiv: https://arxiv.org/abs/2411.04397 · HTML: https://mlsanity.com/paper/2411.04397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Intensity-Free Learning of Temporal Point Processes](https://mlsanity.com/paper/1909.12127/) (2019)
- [Subspace Inference for Bayesian Deep Learning](https://mlsanity.com/paper/1907.07504/) (2019)
- [Neural Spatio-Temporal Point Processes](https://mlsanity.com/paper/2011.04583/) (2020)
- [Transformer Embeddings of Irregularly Spaced Events and Their Participants](https://mlsanity.com/paper/2201.00044/) (2021)
- [Bayesian Deep Learning via Subnetwork Inference](https://mlsanity.com/paper/2010.14689/) (2020)
- [EasyTPP: Towards Open Benchmarking Temporal Point Processes](https://mlsanity.com/paper/2307.08097/) (2023)
- [Do Bayesian Neural Networks Need To Be Fully Stochastic?](https://mlsanity.com/paper/2211.06291/) (2022)
- [HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences](https://mlsanity.com/paper/2210.01753/) (2022)
- [COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution](https://mlsanity.com/paper/1507.02293/) (2015)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [Bayesian Repulsive Gaussian Mixture Model](https://mlsanity.com/paper/1703.09061/) (2017)
- [Group Network Hawkes Process](https://mlsanity.com/paper/2002.08521/) (2020)
- [The Ginibre Point Process as a Model for Wireless Networks with Repulsion](https://mlsanity.com/paper/1401.3677/) (2014)
- [Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data](https://mlsanity.com/paper/2106.00072/) (2021)
- [Determinantal point process mixtures via spectral density approach](https://mlsanity.com/paper/1705.05181/) (2017)
- [Is infinity that far? A Bayesian nonparametric perspective of finite mixture models](https://mlsanity.com/paper/1904.09733/) (2019)
- [MCMC computations for Bayesian mixture models using repulsive point processes](https://mlsanity.com/paper/2011.06444/) (2020)
- [Bayesian Inference for Latent Biologic Structure with Determinantal Point Processes (DPP)](https://mlsanity.com/paper/1506.08253/) (2015)
- [Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning](https://mlsanity.com/paper/2306.14336/) (2023)

## Built on by (most cited first, 1 of 1)

- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)

## Abstract (arXiv)

Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP2DP2) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics.