@previous (B)
Is shtick trans, praytell?
@previous (Analyticity)
I am not sure how you managed guesses that total both 140% and <60% that are equally wrong, but I am impressed! I don't know that I could be that confusingly wrong if I tried.
@previous (Fake anon !ZkUt8arUCU)
Actually it's an estimation of HDI which is confidence in statistical significance of correlation between posterior and prior distribution. Every individual is considered when correlating indicators - this is because one individual could be presenting multiple identities and a truly useful and correctly predictive model would be able to correlate all presented identities to the same unique individual to better than 95% HDI which is when null hypothesis rejection indicates a strongly confident correlation. So, technically it is a measure of confidence that the prior belief, "the one who smelt it dealt it," is wrong, and you just gave me the posterior -and accurate posterior, "the one who supplied it denied it," to update the prior and will lead to a better, more accurate predictive model.
You don't have to try to be confused about complex analysis and predictive probability distributions. But, as it happens you were wrong about the guesses being estimated as right, they are assertions of confidence they are wrong -unless they're right, which they clearly aren't because the confidence index is asserting that they are wrong... and they aren't totals, and they aren't summed together.
So. Yeah, I mean you can be totally more confusingly wrong if you tried to think you were guessing you were right.
But, I am intentionally not explaining everything so I can see how your assertions come from having incomplete information to form the hypothesis and that's not your fault. That's my fault.
I'm just testing the confidence index. Which is making assumptions from an empty dataset so I expected no correlation, I'm not sure why anything is at 90% or higher. These are just for testing how I want the bot behavior when scraping threads for posterior parameters to update prior inferences.
Like, c'mon fake Anon, when have you ever known me to be impressively unimpressive?