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Tech Report kmi-97-22 Abstract


Parameter Estimation in Bayesian Networks from Incomplete Databases
Techreport ID: kmi-97-22
Date: 1997
Author(s): Marco Ramoni and Paola Sebastiani
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Current methods to learn Bayesian Networks from incomplete databases share the common assumption that the unreported data are missing at random. This paper describes a method - called Bound and Collapse (BC) - to learn Bayesian Networks from incomplete databases which allows the analyst to efficiently integrate the information provided by the database and the exogenous knowledge about the pattern of missing data. BC starts by bounding he set of estimates consistent with the available information and then collapses the resulting set to a point estimate via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Experiments comparing BC to the Gibbs Samplings are also provided. 1. Knowledge Media Institute, The Open University. 2. Department of Actuarial Science and Statistics, City University.
 
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Future Internet is...


Future Internet
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:

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  • Mobility for users who expect a seamless experience across spaces, devices, and velocities