objective-vs-subjective-bayesian-currents
IN premise — summaries/2026/08/24/wiki-Bayesian_inference-chunk-4.md
Created 2026-08-25T02:58:44+00:00
Modern Bayesian practice splits into two currents: objective (non-informative) Bayesianism, where analysis depends only on model, data, and prior-assignment rule, and subjective (informative) Bayesianism, where priors encode the analyst's beliefs.
Summary
Modern Bayesian statistics is really two competing philosophies in one label: a neutral school that lets a standard default rule set the starting point, and a judgment school that openly folds the analyst's own expectations into the calculation. This split matters because any Bayesian result is only meaningful once you know which camp produced it, since the starting assumptions are doing a lot of invisible work and the two schools will often land on different answers for the same data.