Inference for mark-censored temporal point processes
Marked temporal point processes (MTPPs) are a general class of stochastic models for
modeling the evolution of events of different types (“marks”) in continuous time. These …
modeling the evolution of events of different types (“marks”) in continuous time. These …
Predictive querying for autoregressive neural sequence models
In reasoning about sequential events it is natural to pose probabilistic queries such as
“when will event A occur next” or “what is the probability of A occurring before B”, with …
“when will event A occur next” or “what is the probability of A occurring before B”, with …
EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
G Kerrigan, K Nelson, P Smyth - arXiv preprint arXiv:2410.07430, 2024 - arxiv.org
Continuous-time event sequences, in which events occur at irregular intervals, are
ubiquitous across a wide range of industrial and scientific domains. The contemporary …
ubiquitous across a wide range of industrial and scientific domains. The contemporary …
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Neural marked temporal point processes have been a valuable addition to the existing
toolbox of statistical parametric models for continuous-time event data. These models are …
toolbox of statistical parametric models for continuous-time event data. These models are …
On the Efficient Marginalization of Probabilistic Sequence Models
A Boyd - 2024 - escholarship.org
Real-world data often exhibits sequential dependence, across diverse domains such as
human behavior, medicine, finance, and climate modeling. Probabilistic methods capture …
human behavior, medicine, finance, and climate modeling. Probabilistic methods capture …