Accession Number:

AD1015363

Title:

Variations on Bayesian Prediction and Inference

Descriptive Note:

Technical Report,29 May 2015,28 Feb 2016

Corporate Author:

University of Illinois - Chicago Chicago United States

Personal Author(s):

Report Date:

2016-05-09

Pagination or Media Count:

14.0

Abstract:

A Bayesian approach, based on updating prior information in light of new observations, via Bayes formula, has both nice intuition and strong theoretical support. However, in some applications, there are some roadblocks to carrying out the Bayesian analysis as usual. This ARO-sponsored project considered two general types of these settings. First, for the problem of predicting a future observation, a flexible Bayesian model is available but is too computationally expensive to implement when data are streaming and fast prediction is required.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE