-----------  zx_distr.eps 

In this file there is a sample of random generated points.
This points are normally distribuited along a line with equation:

 x = a + b*z

where the values for the two paramater are

 a = 0.0005

 b = 0.0007	

and the vertical distribution (x) of the points 
about the line has a sigma of 0.0003 (the same sigma that the
primary vertces are suppose to have about the beam). the orizontal
points are generated also with a gaussian with sigma_z = 28.



--------------- DKF_mc_a_evolution.eps 
                DKF_mc_b_evolution.eps

These two plot show the evolution of the actual fitted value of
the two parameters each time a new measure comes up for the 
Discrete Kalman Filter Algorithm (DKF). Consistently with the
implementation of the DKF a measure consists of two primary vertices,
that means that 500 measures are 1000 of primary vertices processed. 
These are measure of the random generated points with the
values for a and b provided above (mc). 

-------------- DKF_mc_a_50evts.0005.eps
               DKF_mc_b_50evts.0007.eps

In there there is a sample of 1000 measures  of samples of 50 primary vertices
(mc) for the DKF algorithm.

-------------- CS_mc_b_50evts.0007.eps
               CS_mc_a_50evts.0005.eps

Same thing as DKF but this time the algorithm is the Least Squares Method.
50 evnts processed each time.

-------------- DKF_mc_a_50evts.0005.eps
               DKF_mc_b_50evts.0007.eps
               CS_mc_b_50evts.0007.eps
               CS_mc_a_50evts.0005.eps

Same study with 30 events samples.