article · Statistical Papers · 2024
Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods
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Bickel, D. R. (2024). Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods. Statistical Papers. https://doi.org/10.1007/s00362-024-01597-3
Bickel, David R. “Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods.” Statistical Papers, 2024, https://doi.org/10.1007/s00362-024-01597-3.
Bickel, David R. 2024. “Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods.” Statistical Papers. https://doi.org/10.1007/s00362-024-01597-3.
Bickel, D.R. (2024) ‘Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods’, Statistical Papers. Available at: https://doi.org/10.1007/s00362-024-01597-3.
@article{bickel2024bayesian,
title = {{Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods}},
author = {Bickel, David R.},
journal = {Statistical Papers},
year = {2024},
doi = {10.1007/s00362-024-01597-3}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.