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Research Frontiers in Temporal Point Process Modeling

Advanced statistical techniques for modeling and predicting event sequences with irregular timestamps and complex temporal dependencies.

Hawkes Processes in High-Dimensional Event Streams
Self-Exciting Dynamics Across Neuronal and Social Networks
Marked Point Processes with Latent Temporal Dependencies
Causal Inference in Continuous-Time Event Data
Neural Representations of Irregular Temporal Event Sequences
Intensity Function Learning Beyond Parametric Bounds
Information Geometry of Nonstationary Point Processes
Multi-Scale Temporal Clustering in Asynchronous Events
Uncertainty Quantification in Temporal Event Prediction
Graph-Structured Hawkes Processes for Complex Systems

All Data Science PhD categories