Credal model averaging for classification: representing prior ignorance and expert opinions
نویسندگان
چکیده
منابع مشابه
Credal Model Averaging for classification: representing prior ignorance and expert opinions
Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal Model Averaging (CMA) addresses this problem by substituting the single prior over the models by a set of priors (credal set). Such approach solves the problem of how to choose the...
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We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging (BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper’s BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which lea...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2015
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2014.07.001