Tech Reports

Tech Report kmi-97-21 Abstract

Bayesian Inference with Missing Data Using Bound and Collapse
Techreport ID: kmi-97-21
Date: 1997
Author(s): Paola Sebastiani and Marco Ramoni

Current Bayesian methods to estimate conditional probabilities from samples with missing data pose serious problems of robustness and computational efficiency. This paper introduces a new method, called Bound and Collapse (BC), able to overcome these problems. When no information is available on the pattern of missing data, BC turns {em bounds} on the possible estimates consistent with the available information. These bounds can be then collapsed to a point estimate using information about the pattern of missing data, if any. Approximations of the variance and of the posterior distribution are proposed, and their accuracy is compared to approximations based on alternative methods in a real data set of polling data subject to non-response. 1. Department of Actuarial Science and Statistics, City University. 2. Knowledge Media Institute, The Open University.

Future Internet

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Future Internet is...

With over a billion users, today's Internet is arguably the most successful human artifact ever created. The Internet's physical infrastructure, software, and content now play an integral part of the lives of everyone on the planet, whether they interact with it directly or not. Now nearing its fifth decade, the Internet has shown remarkable resilience and flexibility in the face of ever increasing numbers of users, data volume, and changing usage patterns, but faces growing challenges in meetings the needs of our knowledge society. Globally, many major initiatives are underway to address the need for more scientific research, physical infrastructure investment, better education, and better utilisation of the Internet. Within Japan, USA and Europe major new initiatives have begun in the area.

To succeed the Future Internet will need to address a number of cross-cutting challenges including:

Scalability in the face of peer-to-peer traffic, decentralisation, and increased openness

Trust when government, medical, financial, personal data are increasingly trusted to the cloud, and middleware will increasingly use dynamic service selection