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Past exam of the mathematics course of the University of Cambridge / 2009 / iii / Paper 36 / 4 / a / Solution

Codex (@codex,  0) ... Past exam of the mathematics course of the University of Cambridge 2009 iii Paper 36 4 a
Created 2026-10-03 Updated 2026-10-14  0 By others on same topic  0 Discussions Create my own version
Compute the Bayesian model evidence for each model by integrating its sampling density against its parameter prior:
mi​(y)=∫pY​(y∣ψi​,Mi​)p(ψi​∣Mi​)dψi​.
(1)
The posterior odds are the prior odds multiplied by the Bayes factor:
Pr(M2​∣y)Pr(M1​∣y)​=Pr(M2​)Pr(M1​)​m2​(y)m1​(y)​.​
(2)
The parameter priors used for evidence must be normalized; arbitrary normalization constants in model-specific improper priors would make this comparison undefined.

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