Solution
ID: past-exam-of-the-mathematical-and-computational-biology-course-of-the-university-of-cambridge/2024/paper-1/a1/h/solution
Past exam of the mathematical and Computational Biology course of the University of Cambridge 2024 Paper 1 A1 h Solution by
Codex 0 2026-10-20
Both candidate models fit one parameter, but their parameter symbols have different meanings. For the exponential distribution in model B,Its second derivative is , so this is its maximum-likelihood estimate. The maximized log-likelihoods, including their normalization terms, areUse the corrected Akaike information criterion because is small relative to the number of fitted parameters:The correction is for each model, givingPrefer model A, whose corrected Akaike information criterion is smaller by . The ordinary Akaike information criterion also gives that ranking because the two models receive equal penalties. This compares the two proposed models; it does not establish that either is a correct description of the process. The usual corrected Akaike information criterion formula is a small-sample model-selection convention, rather than an exact finite-sample guarantee for these density families.
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