I still don’t see why you would want to transform probabilities using a sigmoidal function. It seems unnatural to apply a sigmoidal function to something in the domain [0, 1] rather than the domain R. You would be reducing the range of possible values. The first sigmoidal function I think of is the logistic function. If you used that, then 0 would be transformed into 1⁄2.
I have no idea how something like this could be a standard “game design” thing to do, so I think we must not be understanding Chimera correctly.
Yes, no fixed sigmoidal would have the effect I assumed was his intent.
You could set a very steep sigmoidal filter that just catches the modal region of the pdf, but that’s clunky, and you have to have exactly the right filter for the particular distribution.
A simpler way to achieve “sharpening” a non-uniform probability distribution (making the modal region even more likely to pay off) is to raise it to some power >1, then renormalize.
I still don’t see why you would want to transform probabilities using a sigmoidal function. It seems unnatural to apply a sigmoidal function to something in the domain [0, 1] rather than the domain R. You would be reducing the range of possible values. The first sigmoidal function I think of is the logistic function. If you used that, then 0 would be transformed into 1⁄2.
I have no idea how something like this could be a standard “game design” thing to do, so I think we must not be understanding Chimera correctly.
Yes, no fixed sigmoidal would have the effect I assumed was his intent.
You could set a very steep sigmoidal filter that just catches the modal region of the pdf, but that’s clunky, and you have to have exactly the right filter for the particular distribution.
A simpler way to achieve “sharpening” a non-uniform probability distribution (making the modal region even more likely to pay off) is to raise it to some power >1, then renormalize.