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OBD talk:BINA/PAR3: Difference between revisions
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[[User:Neo|Neo]] | [[User:Neo|Neo]] | ||
:The term "inverse normal" is apparently not conventional, and also rather confusing because it can be mistaken as referring to either the [[wikipedia:Normal-inverse_Gaussian_distribution|normal-inverse Gaussian distribution]] or the [[wikipedia:Inverse_Gaussian_distribution|inverse Gaussian distribution]], both of which are rather exotic and irrelevant here. | :The term "inverse normal" is apparently not conventional, and also rather confusing because it can be mistaken as referring to either the [[wikipedia:Normal-inverse_Gaussian_distribution|normal-inverse Gaussian distribution]] or the [[wikipedia:Inverse_Gaussian_distribution|inverse Gaussian distribution]], both of which are rather exotic and irrelevant here. | ||
:What we have here is the inverse of the [[wikipedia:Error_function|error function]] or rather that of [[wikipedia:Normal_distribution#Standard_deviation_and_confidence_intervals|erfc(x/sqrt(2))]]. If you have Java installed, [ | :What we have here is the inverse of the [[wikipedia:Error_function|error function]] or rather that of [[wikipedia:Normal_distribution#Standard_deviation_and_confidence_intervals|erfc(x/sqrt(2))]]. If you have Java installed, [https://onlinestatbook.com/analysis_lab/inverse_normal_dist.html HERE] is a nice applet that you can toy around with to see just what the table in your PDF link corresponds to. | ||
:As further pointed out [[wikipedia:Normal_distribution#Generating_values_for_normal_random_variables|HERE]], inverting the [[wikipedia:Normal_distribution#Cumulative_distribution_function|standard normal cdf]] gives you a way to generate ''normally'' distributed random variables from a ''uniformly'' distributed random variable, which is exactly what Oni does (see Neo's code sample above). | :As further pointed out [[wikipedia:Normal_distribution#Generating_values_for_normal_random_variables|HERE]], inverting the [[wikipedia:Normal_distribution#Cumulative_distribution_function|standard normal cdf]] gives you a way to generate ''normally'' distributed random variables from a ''uniformly'' distributed random variable, which is exactly what Oni does (see Neo's code sample above). | ||
:The float r is a ''uniformly'' distributed random variable in (-1.0,1.0) (@ Neo: please check). Same for x except the interval is now [0.0,10.0). z is a first approximation of erfc(0.0998 * x / sqrt(2)), interpolated linearly between erfc(0.0998 * floorf(x) / sqrt(2)) and erfc(0.0998 * (floorf(x) + 1) / sqrt(2)). The table is thus sampled uniformly. | :The float r is a ''uniformly'' distributed random variable in (-1.0,1.0) (@ Neo: please check). Same for x except the interval is now [0.0,10.0). z is a first approximation of erfc(0.0998 * x / sqrt(2)), interpolated linearly between erfc(0.0998 * floorf(x) / sqrt(2)) and erfc(0.0998 * (floorf(x) + 1) / sqrt(2)). The table is thus sampled uniformly. | ||