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Maximizing the information learned from finite data selects a simple model
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. When many parameters are po...
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| Udgivet i: | Proc Natl Acad Sci U S A |
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| Main Authors: | , , , |
| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
National Academy of Sciences
2018
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| Fag: | |
| Online adgang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5828598/ https://ncbi.nlm.nih.gov/pubmed/29434042 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1715306115 |
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