Dan Simpson • 9/2/2022

Priors part 4: Specifying priors that appropriately penalise complexity

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This article is part of a series on prior distributions in Bayesian statistics. It critiques common prior-setting methods and introduces the concept of penalized complexity priors. The author explores the philosophical nature of parameters, compares model parameterizations (like Negative-Binomial vs. Poisson-Gamma), and argues for priors that naturally penalize unnecessary model complexity to avoid overfitting.

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#Machine Learning #Statistical Modeling #Bayesian Statistics
Priors part 4: Specifying priors that appropriately penalise complexity

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