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From: Deane Yang (deane_yang_at_[hidden])
Date: 20060710 16:40:56
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 1dimensional probability distribution, do
they not?
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