# Poisson Random Fields for Dynamic Feature Models

arXiv: https://arxiv.org/abs/1611.07460 · HTML: https://mlsanity.com/paper/1611.07460/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

## Built on by (most cited first, 7 of 7)

- [Neural Dynamic Focused Topic Model](https://mlsanity.com/paper/2301.10988/) (2023)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [Learning Topic Models: Identifiability and Finite-Sample Analysis](https://mlsanity.com/paper/2110.04232/) (2021)
- [Communication-efficient k-Means for Edge-based Machine Learning](https://mlsanity.com/paper/2102.04282/) (2021)
- [Speeding up PCA with priming](https://mlsanity.com/paper/2109.03709/) (2021)
- [Improving the Inference of Topic Models via Infinite Latent State Replications](https://mlsanity.com/paper/2301.12974/) (2023)
- [Logistic-beta processes for dependent random probabilities with beta marginals](https://mlsanity.com/paper/2402.07048/) (2024)

## Abstract (arXiv)

We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. This model is suitable as a prior in Bayesian nonparametric feature allocation models in which the features underlying the observed data exhibit a dependency structure over time. More specifically, we establish a new framework for generating dependent Indian buffet processes, where the Poisson random field model from population genetics is used as a way of constructing dependent beta processes. Inference in the model is complex, and we describe a sophisticated Markov Chain Monte Carlo algorithm for exact posterior simulation. We apply our construction to develop a nonparametric focused topic model for collections of time-stamped text documents and test it on the full corpus of NIPS papers published from 1987 to 2015.