class TRandom: public TNamed

 TRandom

 basic Random number generator class (periodicity = 10**9).
 Note that this is a very simple generator (linear congruential)
 which is known to have defects (the lower random bits are correlated)
 and therefore should NOT be used in any statistical study.
 One should use instead TRandom1, TRandom2 or TRandom3.
 TRandom3, is based on the "Mersenne Twister generator", and is the recommended one,
 since it has good random proprieties (period of about 10**6000 ) and it is fast.
 TRandom1, based on the RANLUX algorithm, has mathematically proven random proprieties
 and a period of about 10**171. It is however slower than the others.
 TRandom2, is based on the Tausworthe generator of L'Ecuyer, and it has the advantage
 of being fast and using only 3 words (of 32 bits) for the state. The period is 10**26.

 The following table shows some timings (in nanoseconds/call)
 for the random numbers obtained using an Intel Pentium 3.0 GHz running Linux
 and using the gcc 3.2.3 compiler

    TRandom           34   ns/call     (BAD Generator)
    TRandom1          242  ns/call
    TRandom2          37   ns/call
    TRandom3          45   ns/call


 The following basic Random distributions are provided:

   -Exp(tau)
   -Integer(imax)
   -Gaus(mean,sigma)
   -Rndm()
   -Uniform(x1)
   -Landau(mpv,sigma)
   -Poisson(mean)
   -Binomial(ntot,prob)

 Random numbers distributed according to 1-d, 2-d or 3-d distributions

 contained in TF1, TF2 or TF3 objects.
 For example, to get a random number distributed following abs(sin(x)/x)*sqrt(x)
 you can do :
   TF1 *f1 = new TF1("f1","abs(sin(x)/x)*sqrt(x)",0,10);
   double r = f1->GetRandom();
 or you can use the UNURAN package. You need in this case to initialize UNURAN
 to the function you would like to generate.
   TUnuran u;
   u.Init(TUnuranDistrCont(f1));
   double r = u.Sample();

 The techniques of using directly a TF1,2 or 3 function is powerful and
 can be used to generate numbers in the defined range of the function.
 Getting a number from a TF1,2,3 function is also quite fast.
 UNURAN is a  powerful and flexible tool which containes various methods for
 generate random numbers for continuous distributions of one and multi-dimension.
 It requires some set-up (initialization) phase and can be very fast when the distribution
 parameters are not changed for every call.

 The following table shows some timings (in nanosecond/call)
 for basic functions,  TF1 functions and using UNURAN obtained running
 the tutorial math/testrandom.C
 Numbers have been obtained on a Intel 2GHz Dual Core running MacOSX and compiled with gcc 4.0.1

  Distribution            nanoseconds/call

                      TRandom  TRandom1 TRandom2 TRandom3

  Rndm..............   24.000  137.000   29.000   30.000
  RndmArray.........   17.000  128.000   22.000   22.000
  Gaus..............   86.000  242.000   92.000   96.000
  Rannor............  141.000  258.000  148.000  147.000
  Landau............   68.000  173.000   73.000   74.000
  Binomial(5,0.5)...  152.000  695.000  171.000  179.000
  Binomial(15,0.5)..  414.000 2060.000  480.000  497.000
  Poisson(3)........  212.000  653.000  231.000  234.000
  Poisson(10).......  402.000 1618.000  456.000  460.000
  Poisson(70)....... 1225.000 1651.000 1253.000 1250.000
  Poisson(100)...... 1233.000 1664.000 1260.000 1262.000
  GausTF1...........  210.000  326.000  218.000  216.000
  LandauTF1.........  209.000  325.000  217.000  213.000
  GausUNURAN........   90.000  202.000   97.000   96.000
  PoissonUNURAN(10).  160.000  361.000  170.000  170.000
  PoissonUNURAN(100)  139.000  347.000  148.000  149.000

  Note that the time to generate a number from an arbitrary TF1 function
  using TF1::GetRandom or using TUnuran is  independent of the complexity of the function.

  TH1::FillRandom(TH1 *) or TH1::FillRandom(const char *tf1name)

  can be used to fill an histogram (1-d, 2-d, 3-d from an existing histogram
  or from an existing function.

  Note this interesting feature when working with objects

  You can use several TRandom objects, each with their "independent"
  random sequence. For example, one can imagine
     TRandom *eventGenerator = new TRandom();
     TRandom *tracking       = new TRandom();
  eventGenerator can be used to generate the event kinematics.
  tracking can be used to track the generated particles with random numbers
  independent from eventGenerator.
  This very interesting feature gives the possibility to work with simple
  and very fast random number generators without worrying about
  random number periodicity as it was the case with Fortran.
  One can use TRandom::SetSeed to modify the seed of one generator.

  a TRandom object may be written to a Root file

    -as part of another object
    -or with its own key (example gRandom->Write("Random");