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Tech Report kmi-98-06 Abstract


Model Selection and Model Averaging with Missing Data
Techreport ID: kmi-98-06
Date: 1998
Author(s): Marco Ramoni and Paola Sebastiani
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Missing data can impair the reliability of statistical inference as they may affect the representativity of the sample. Nonetheless, under some conditions guaranteeing that the missing data mechanism is ignorable, reliable conclusions can be still drawn from the incomplete sample. Ignorability conditions are well-understood for parameter estimation but when the inference task involves the computation of the posterior probability of a data model, as required by Bayesian model selection and prediction through model averaging, these conditions are not sufficient. This paper defines new ignorability conditions for model selection and model averaging from incomplete data. 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.