Paul A Bristow wrote:
| -----Original Message----- | From: Kevin Lynch
| Why not hide the functions behind a class interface? After all, the | various functions are "properties" of the distributions. Hence: | | class students_t { | students_t(double mu); | double P(double x); | double Q(double x); | double invP(double p); (or perhaps inverseP or Pinv or | something) | ..... | } | | class normal { | normal(double mu, double sigma); | double P(double x); | double Q(double x); | double invP(double x); | ...... | }
Rather interesting idea.
I support Kevin's proposal rather strongly for exactly the reasons he states. But I'm not sure what P, Q, invP mean. I would prefer: double density(double x); double cumulative(double x); double inverse_cumulative(double y);
How would you envisage this working with Fisher, for example which has degrees of freedom 1 and 2, and a variance ratio.
Is this a 1D or 2D or 3D?
Its inversion will return df1 (given df2 and F and Probability) or df2 (given df1, F and Prob) or F (given Df1 and df2 and Prob)
WOuld you like to flesh out how you suggest handling all these?
Could you clarify your question? Isn't the F distribution still the probability distribution of a single real random variable? The cumulative and inverse cumulative density functions have a consistent mathematical meaning for any 1-dimensional probability distribution, do they not?