Maximizing the information learned from finite data selects a simple model

2 May 2017Henry H. MattinglyMark K. TranstrumMichael C. AbbottBenjamin B. Machta

We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We advocate for the prior which maximizes the mutual information between parameters and predictions, learning as much as possible from limited data... (read more)

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