New tools for recursive Bayesian inference applied to high-resolution satellite imagery

Music City Center 

Recursive Bayesian inference, in which posterior beliefs are updated in light of accumulating batches of data, is a tool for implementing Bayesian models in applications with streaming and/or very large data sets. Implementation typically proceeds via a sequence of "transient" posteriors characterized by samples obtained using acceptance/rejection algorithms in which draws from one posterior in the sequence are used as proposals for the next. While straightforward to implement, such filtering approaches suffer from particle depletion, degrading each sample's ability to represent its target posterior. Generating proposals from smoothed versions of the transient posterior's empirical sampling distributions can alleviate particle depletion, but the efficiency of such an approach can be extremely limited for moderate to high dimensional parameter spaces. We introduce new tools for smoothed recursive Bayesian inference in the form of blocking and generalized elliptical slice samplers that ensure satisfactory effective sample sizes throughout the sequence of transient posterior samples. We apply the method to satellite imagery to classify forest vegetation in New Mexico.

recursive Bayes

generalized elliptical slice sampler

satellite imagery

vegetation cover