# Lecture Notes: Temporal Point Processes and the Conditional Intensity Function

arXiv: https://arxiv.org/abs/1806.00221 · HTML: https://mlsanity.com/paper/1806.00221/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 30 of 40)

- [Edit Flows: Flow Matching with Edit Operations](https://mlsanity.com/paper/2506.09018/) (2025)
- [Spatio-temporal Diffusion Point Processes](https://mlsanity.com/paper/2305.12403/) (2023)
- [Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes](https://mlsanity.com/paper/2201.12569/) (2022)
- [A Graph Regularized Point Process Model For Event Propagation Sequence](https://mlsanity.com/paper/2211.11758/) (2022)
- [Decomposable Transformer Point Processes](https://mlsanity.com/paper/2409.18158/) (2024)
- [On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data](https://mlsanity.com/paper/2306.17066/) (2023)
- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [Interacting Diffusion Processes for Event Sequence Forecasting](https://mlsanity.com/paper/2310.17800/) (2023)
- [In-Context Learning of Temporal Point Processes with Foundation Inference Models](https://mlsanity.com/paper/2509.24762/) (2025)
- [Edit-Based Flow Matching for Temporal Point Processes](https://mlsanity.com/paper/2510.06050/) (2025)
- [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2401.04612/) (2024)
- [Detecting Anomalous Event Sequences with Temporal Point Processes](https://mlsanity.com/paper/2106.04465/) (2021)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2406.06149/) (2024)
- [Byte-token Enhanced Language Models for Temporal Point Processes Analysis](https://mlsanity.com/paper/2502.07139/) (2025)
- [The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks](https://mlsanity.com/paper/2205.00639/) (2022)
- [Neural Spatiotemporal Point Processes: Trends and Challenges](https://mlsanity.com/paper/2502.09341/) (2025)
- [A Differentiable Point Process with Its Application to Spiking Neural Networks](https://mlsanity.com/paper/2106.00901/) (2021)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [RoTHP: Rotary Position Embedding-based Transformer Hawkes Process](https://mlsanity.com/paper/2405.06985/) (2024)
- [Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches](https://mlsanity.com/paper/2501.14291/) (2025)
- [TPP-SD: Accelerating Transformer Point Process Sampling with Speculative Decoding](https://mlsanity.com/paper/2507.09252/) (2025)
- [Learning Point Processes using Recurrent Graph Network](https://mlsanity.com/paper/2208.05736/) (2022)
- [Online Learning for Mixture of Multivariate Hawkes Processes](https://mlsanity.com/paper/2208.07961/) (2022)
- [How Stable is Knowledge Base Knowledge?](https://mlsanity.com/paper/2211.00989/) (2022)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network](https://mlsanity.com/paper/2206.03469/) (2022)
- [Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process](https://mlsanity.com/paper/2301.09948/) (2023)
- [Predicting the future with a scale-invariant temporal memory for the past](https://mlsanity.com/paper/2101.10953/) (2021)
- [Influence Estimation and Maximization via Neural Mean-Field Dynamics](https://mlsanity.com/paper/2106.02608/) (2021)

## Abstract (arXiv)

These short lecture notes contain a not too technical introduction to point processes on the time line. The focus lies on defining these processes using the conditional intensity function. Furthermore, likelihood inference, methods of simulation and residual analysis for temporal point processes specified by a conditional intensity function are considered.