Poisson Random Fields for Dynamic Feature Models
Explore a dynamic feature allocation model for time-stamped document collections in this 30-minute talk by Valerio Perrone from the Oxford-Warwick Statistics Programme. Delve into the Wright-Fisher Indian Buffet Process (WF-IBP), a Bayesian nonparametric model that extends the static Indian Buffet Process to account for changing topic popularity over time. Learn how this model is applied to develop a nonparametric focused topic model for analyzing the full corpus of NIPS papers from 1987 to 2015. Gain insights into the motivation, mathematical foundations, and practical applications of the WF-IBP, including its development based on population genetic models and Poisson random fields. Compare dynamic and static models using simulated data and real-world NIPS data, and understand the advantages of this approach in capturing evolving topics in large-scale document collections.