Dear HSE

i have to say im not that confident at handling all the marvelous stuff Raymond has provided. The computation of probability is relatively simple but the fitting it to the data is not

Example data set

[3,2];[2,5];[4,6,1];[2,11,2];[4,13,5];[3,17,10]'[4,22,15,1];[2,27,25,2] ........[2,435,10759,86440,284674,411687,258279,64131,5033,65],[4,544,11593,92883,316709,476204,312411,82577,7121,110],....

where the length of sequence extends according to a rule ive established as 1+ [n(n+1)/2] The data you'll notice has a very Poisson like distribution

BUT the k and lambda are not discrete so in order to match the distribution to the data it will be necessary to vary k and lambda to match the profile

I suppose people might have wondered reading the first post what i was doing as Poisson is not immediately obvious as a best fit solution !!!

i hope this sort of explains what im thinking of doing in trying to establish a connection between non linear and discrete functions this way

regards mike b