## maxima-commits

 [Maxima-commits] CVS: maxima/doc/info distrib.texi,1.12,1.13 From: Alexey Beshenov - 2008-12-28 17:37:42 Update of /cvsroot/maxima/maxima/doc/info In directory 23jxhf1.ch3.sourceforge.com:/tmp/cvs-serv1813 Modified Files: distrib.texi Log Message: @ifinfo & @ifhtml -> @ifnottex Index: distrib.texi =================================================================== RCS file: /cvsroot/maxima/maxima/doc/info/distrib.texi,v retrieving revision 1.12 retrieving revision 1.13 diff -u -d -r1.12 -r1.13 --- distrib.texi 5 Dec 2008 18:50:23 -0000 1.12 +++ distrib.texi 28 Dec 2008 17:37:35 -0000 1.13 @@ -145,16 +145,11 @@ $$V\left[X\right]=\sum_{x_{i}}{f\left(x_{i}\right)\left(x_{i}-E\left[X\right]\right)^2},$$ @end tex -@... -@... - D[X] = sqrt(V[X]), -@... example -@... ifhtml -@... +@ifnottex @example D[X] = sqrt(V[X]), @end example -@... ifinfo +@end ifnottex @tex $$D\left[X\right]=\sqrt{V\left[X\right]},$$ @end tex @@ -480,17 +475,7 @@ Returns a Student random variate @math{t(n)}, with @math{n>0}. Calling @code{random_student_t} with a second argument @var{m}, a random sample of size @var{m} will be simulated. The implemented algorithm is based on the fact that if @var{Z} is a normal random variable @math{N(0,1)} and @math{S^2} is a chi square random variable with @var{n} degrees of freedom, @math{Chi^2(n)}, then -@... -@... - Z - X = ------------- - / 2 \ 1/2 - | S | - | --- | - \ n / -@... example -@... ifhtml -@... +@ifnottex @example Z X = ------------- @@ -499,7 +484,7 @@ | --- | \ n / @end example -@... ifinfo +@end ifnottex @tex $$X={{Z}\over{\sqrt{{S^2}\over{n}}}}$$ @end tex @@ -651,17 +636,7 @@ Returns a noncentral Student random variate @math{nc_t(n,ncp)}, with @math{n>0}. Calling @code{random_noncentral_student_t} with a third argument @var{m}, a random sample of size @var{m} will be simulated. The implemented algorithm is based on the fact that if @var{X} is a normal random variable @math{N(ncp,1)} and @math{S^2} is a chi square random variable with @var{n} degrees of freedom, @math{Chi^2(n)}, then -@... -@... - X - U = ------------- - / 2 \ 1/2 - | S | - | --- | - \ n / -@... example -@... ifhtml -@... +@ifnottex @example X U = ------------- @@ -670,7 +645,7 @@ | --- | \ n / @end example -@... ifinfo +@end ifnottex @tex $$U={{X}\over{\sqrt{{S^2}\over{n}}}}$$ @end tex @@ -1136,20 +1111,13 @@ Returns a F random variate @math{F(m,n)}, with @math{m,n>0}. Calling @code{random_f} with a third argument @var{k}, a random sample of size @var{k} will be simulated. The simulation algorithm is based on the fact that if @var{X} is a @math{Chi^2(m)} random variable and @math{Y} is a @math{Chi^2(n)} random variable, then -@... -@... - n X - F = --- - m Y -@... example -@... ifhtml -@... +@ifnottex @example n X F = --- m Y @end example -@... ifinfo +@end ifnottex @tex $$F={{n X}\over{m Y}}$$ @end tex