*
* Generation of graph on p 471
*
data(unit=input) 1 10 c
 5 0 1 1 0 3 2 3 4 1
*
* This computes the Poisson likelihood for the sample. While we're showing this
* as an example, this is a type of calculation that is best done in logarithms:
* with a larger data set, or much larger numbers, it's quite possible for the
* probability calculation to underflow, that is, become smaller than the smallest
* positive number that can be represented in the computer's floating point
* registers.
*
function poissonL theta
local integer i
compute poissonL=1.0
do i=1,10
   compute poissonL=poissonL*exp(-theta)*theta**c(i)/%factorial(c(i))
end do i
end
*
set grid 1 100 = .5+.03*t
set like 1 100 = poissonL(grid(t))*10**7
set llike 1 100 = log(like)+25
*
scatter(style=line,overlay=line,ovlabel="log L + 25",vlabel="Likelihood x 10**7",$
   footer="Figure 17.1 Likelihood and Log-Likelihood Functions for a Poisson Distribution") 2
# grid like
# grid llike