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 an Intel Xeon Quad-core Harpertown (E5410) 2.33 GHz running
 Linux SLC4 64 bit and compiled with gcc 3.4

 Distribution            nanoseconds/call
                     TRandom  TRandom1 TRandom2 TRandom3
 Rndm..............    5.000  105.000    7.000   10.000
 RndmArray.........    4.000  104.000    6.000    9.000
 Gaus..............   36.000  180.000   40.000   48.000
 Rannor............  118.000  220.000  120.000  124.000
 Landau............   22.000  123.000   26.000   31.000
 Exponential.......   93.000  198.000   98.000  104.000
 Binomial(5,0.5)...   30.000  548.000   46.000   65.000
 Binomial(15,0.5)..   75.000 1615.000  125.000  178.000
 Poisson(3)........   96.000  494.000  109.000  125.000
 Poisson(10).......  138.000 1236.000  165.000  203.000
 Poisson(70).......  818.000 1195.000  835.000  844.000
 Poisson(100)......  837.000 1218.000  849.000  864.000
 GausTF1...........   83.000  180.000   87.000   88.000
 LandauTF1.........   80.000  180.000   83.000   86.000
 GausUNURAN........   40.000  139.000   41.000   44.000
 PoissonUNURAN(10).   85.000  271.000   92.000  102.000
 PoissonUNURAN(100)   62.000  256.000   69.000   78.000

  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");