This feels like it’s a specific instance of a more general thing around how fast I can converge on a guess about a distribution I’m sampling from. Imagine the scatterplot with data points added one at a time. There are both negative guesses (this point rules out these distributions) and positive guesses (it sorta looks like this will converge to a bimodal distribution). Depending on payoff structure and priors I might want to lean more heavily towards faster/sparser guesses. I’m not up on current ML but this has to be common enough to be a named thing.
This feels like it’s a specific instance of a more general thing around how fast I can converge on a guess about a distribution I’m sampling from. Imagine the scatterplot with data points added one at a time. There are both negative guesses (this point rules out these distributions) and positive guesses (it sorta looks like this will converge to a bimodal distribution). Depending on payoff structure and priors I might want to lean more heavily towards faster/sparser guesses. I’m not up on current ML but this has to be common enough to be a named thing.