Generative Filtering for Recursive Bayesian Inference with Streaming Data

TL;DR

Generative Filtering is a novel recursive Bayesian update method for streaming data that maintains efficiency and accuracy over time by preventing distribution degeneration through parallel updates and convergence guarantees.

Contribution

It introduces Generative Filtering, a new approach that improves recursive Bayesian inference in streaming data by avoiding distribution degeneration and enabling efficient updates with theoretical convergence guarantees.

Findings

Generative Filtering prevents distribution degeneration in streaming Bayesian updates.

The method achieves comparable speed to traditional filtering methods.

Simulation and ecological data demonstrate improved inference stability.

Abstract

In the streaming data setting, where data arrive continuously or in frequent batches and there is no pre-determined amount of total data, Bayesian models can employ recursive updates, incorporating each new batch of data into the model parameters' posterior distribution. Filtering methods are currently used to perform these updates efficiently, however, they suffer from eventual degradation as the number of unique values within the filtered samples decreases. We propose Generative Filtering, a method for efficiently performing recursive Bayesian updates in the streaming setting. Generative Filtering retains the speed of a filtering method while using parallel updates to avoid degenerate distributions after repeated applications. We derive rates of convergence for Generative Filtering and conditions for the use of sufficient statistics instead of fully storing all past data. We investigate the alleviation of filtering degradation through simulation and Ecological species count data.