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Tech Report kmi-97-07 Abstract


The Use of Exogenous Knowledge to Learn Bayesian Networks from Incomplete Databases
Techreport ID: kmi-97-07
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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Knowledge Management is...


Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.