Study · Topic 4
Bayesian inference for PDFs — posterior, evidence, nested sampling
The statistical engine of this lab, derived from first principles. Each section below is a full chapter: complete mathematical treatment, derivations, diagrams, and worked examples.
4.1Bayes for proton structure; priorsThe rule aimed at function inference; prior design and prior sensitivity, honestly.
4.2Nested sampling, derivedVolume-shrinkage statistics, the evidence estimator and its error budget, slice sampling at high dimension.
4.3Evidence, Bayes factors, and complexityThe Occam factor via Laplace, model comparison scales, Bayesian complexity derived.
4.4Closure tests and calibrationL0/L1 statistics derived — expected χ², coverage, the health identity — and why certificates are ratio-specific.