Understanding and efficiently representing skills is one of the most important problems in a general Programming by Demonstration (PbD) paradigm. We present Growing Hierarchical Dynamic Bayesian Networks (GHDBN), an adaptive variant of the general DBN model able to learn and to represent complex skills. The structure of the model, in terms of number of states and possible transitions between them, is not needed to be known a priori. Learning in the model is performed incrementally and in an unsupervised manner.
Background:In search of a proposed viral aetiology of childhood acute lymphoblastic leukaemia (ALL), the common species C adenoviruses were analysed in Guthrie cards.Methods:Guthrie cards from 243 children who later developed ALL and from 486 matched controls were collected and analysed by nested polymerase chain reaction for the presence of adenovirus DNA.Results:Adenovirus DNA was reliably detected from only two subjects, both of whom developed ALL.Conclusion:Adenovirus DNA is detected in Guthrie card samples at too low a frequency to reveal an association between adenovirus and the development of leukaemia.
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