Generalized Bayesian Regression and Model Learning
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We propose a generalized Bayesian regression and model learning tool based on the “Bayesian Validation Metric" (BVM) proposed in [1], called BVM model learning. This method performs Bayesian regression based on a user's definition of model-data agreement and allows for model selection on any type of data distribution, unlike Bayesian and standard regression techniques, that fail in some cases. We show that the BVM model learning is capable of representing and combining Bayesian and standard regression techniques in a single framework and generalizing these methods. Thus, this tool offers new insights into the interpretation of the predictive envelopes in Bayesian and standard regression while giving the modeler more control over these envelopes.
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