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Regression with known upper bounds and lower bounds of predicted variables

It sounds like what you want is tobit regression, which is designed for data exactly like yours, where any predicted values above an upper bounds are set to that upper bound and values below a lower ...
Noah's user avatar
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Sampling to maximise f(x)p(x)

"and then reject some of the samples " You end up with a sample that follows an alternative distribution $\propto f(x)p(x)$ and still have to do something like KDE to estimate the mode of ...
Sextus Empiricus's user avatar

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