It’s also very helpful to know things like why someone might go around squaring differences and then summing them, and what kinds of situations that makes sense in. That way you can tell when you make errors of interpretation. For example, “differences pertaining to the squared” is a plausible but less likely interpretation of “squared differences”, but knowing that people commonly square differences and then sum them in order to calculate an L₂ norm, often because they are going to take the derivative of the result so as to solve for a local minimum, makes that a much less plausible interpretation.
And for a Bayesian to be rational in the colloquial sense, they must always remember to assign some substantial probability weight to “other”. For example, you can’t simply assume that words like “sum” and “differences” are being used with one of the meanings you’re familiar with; you must remember that there’s always the possibility that you’re encountering a new sense of the word.
It’s also very helpful to know things like why someone might go around squaring differences and then summing them, and what kinds of situations that makes sense in. That way you can tell when you make errors of interpretation. For example, “differences pertaining to the squared” is a plausible but less likely interpretation of “squared differences”, but knowing that people commonly square differences and then sum them in order to calculate an L₂ norm, often because they are going to take the derivative of the result so as to solve for a local minimum, makes that a much less plausible interpretation.
And for a Bayesian to be rational in the colloquial sense, they must always remember to assign some substantial probability weight to “other”. For example, you can’t simply assume that words like “sum” and “differences” are being used with one of the meanings you’re familiar with; you must remember that there’s always the possibility that you’re encountering a new sense of the word.