What visualization I should choose for Monte Carlo simulations in timeline events?
Instead of  (or additionally to) histogram you may check the density plot of the distribution of the intervals between the 
events. In the r snipped below the process id identified with  ID and the interval length is INT_LEN .

densityplot(~INT_LEN,  groups = ID,
data =  df,    
scales=list(x=list(rot=90, cex= .9),y=list(cex=.9)),par.strip.text=list(cex=.8), plot.points = F,  
ylab="density", xlab="Interval between Events", main=paste("Interval Length Density per Process"  ) )




This show the distribution (in my simple case there are two different distribution). One group of processes has a normal distribution of event with a mean of 1,2. So you may argue that there are 50 events per minute on average.

But this may not be true as the time sequence is lost in this view.

To get the time series information I select a basic interval length, where the distribution of the event doesn't play a role any more.
I use a minute, but if this is to coarse go down. The point is there is no difference to get all events at the beginning of the interval (minute)
or uniformly distribute within the interval.

This selection allows to aggregate the data on the interval level and present it as a time series. The graph below shows the count of event per minute.

xy &lt;- xyplot(CNT ~ MINUTE_NO , groups = ID,   
data =  df ,    type=c("o","g"),
scales=list(rot=90, x=list( cex= .9 ),y=list(rot=0, cex=.8, alternating = c(1,1), tck=c(1,0), relation = "free")),par.strip.text=list(cex=.8), 
ylab="event count per minute", xlab="time", main=paste("Time Series - Event Count per Minute"  ) )
print (xy)




Here you can spot a possible trend in the distribution of the events.