= Posterior equality for proportional likelihoods
{title2=$L_i'=hL_i\ \Longrightarrow\ \pi_i'=\pi_i$}
If every model's <likelihood> is multiplied by the same positive factor independent of its parameter or model index, <Bayes theorem> cancels that factor from the normalized posterior. Fixed-count Bernoulli sampling and sampling stopped at a specified positive head count give such proportional <likelihoods> for the same total heads and tails. This conclusion uses their actual sampling <likelihoods>; it is not a claim that every selection or stopping rule can be ignored.
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