Working paper Open Access
An explanatory rationale for priors sharpened into Occam's razors
Bickel, David R.
In Bayesian statistics, if the distribution of the data is unknown, then each plausible distribution of the data is indexed by a parameter value, and the prior distribution of the parameter is specified. To the extent that more complicated data distributions tend to require more coincidences for their construction than simpler data distributions, default prior distributions should be transformed to assign additional prior probability or probability density to the parameter values that refer to simpler data distributions. The proposed transformation of the prior distribution relies on the entropy of each data distribution as the relevant measure of complexity. The transformation is derived from a few first principles and extended to stochastic processes.
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- Publication date:
- September 10, 2018
- DOI:
-
- Keyword(s):
- explanatory coherence foundations of Bayesian statistics objective prior distribution Ockham's razor informative prior distribution objective Bayes sharpened prior distribution simplicity postulate
- License (for files):
- Creative Commons Attribution-NonCommercial-NoDerivatives