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Notes -
"prior" is obviously from bayesian statistics, where you update a "prior" distribution with new information, producing a "posterior" distribution. The "prior" may be that most people who go to night clubs or get drunk with models are unsuited for high office. But that's just a prior - she already is PM, her actions and history tell you enough about how good of a PM she is that that prior distribution doesn't matter much. In bayes-terms, you have enough new information about her, instead of her being one of the hundred million people who go to clubs or get drunk, that the "prior" doesn't mean much and bears little resemblance to the posterior - but even that's kind of a distraction.
But even that prior doesn't really make sense, tbh, can you try to justify it more?
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