Title

Online Bayesian Analysis

Authors

Ruibin Xi, Washington University in St Louis
Yongjin Kim, Washington University in St Louis
Nan Lin, Washington University in St Louis
Yixin Chen, Washington University in St LouisFollow
Gruia-Catalin Roman, Washington University in St Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

2009

Filename

wucse-2009-30.pdf

DOI:

10.7936/K7SN076H

Technical Report Number

wucse-2009-30

Abstract

In the last few years, there has been active research on aggregating advanced statistical measures in multidimensional data cubes from partitioned subsets of data. In this paper, we propose an online compression and aggregation scheme to support Bayesian estimations in data cubes based on the asymptotic properties of Bayesian statistics. In the proposed approach, we compress each data segment by retaining only the model parameters and a small amount of auxiliary measures. We then develop an aggregation formula that allows us to reconstruct the Bayesian estimation from partitioned segments with a small approximation error. We show that the Bayesian estimates and the aggregated Bayesian estimates are asymptotically equivalent.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7SN076H

Recommended Citation

Xi, Ruibin; Kim, Yongjin; Lin, Nan; Chen, Yixin; and Roman, Gruia-Catalin, "Online Bayesian Analysis" Report Number: wucse-2009-30 (2009). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/15