how to input array into Poisson Probability distribution?

Given an array data for single photon count, [1,100], how do I input such data into a Poisson distribution function? http://en.wikipedia.org/wiki/Poisson_distribution
The aim is to check if the noise emanating from single photon count is indeed Poisson. Thank you!

Respuestas (1)

Star Strider
Star Strider el 13 de Abr. de 2015
I am not certain I understand exactly what you want to do, but fitting your data with poissfit, or histfit specifying the 'poisson' distribution, will likely provide you with the information you want.

3 comentarios

Star Strider
Star Strider el 13 de Abr. de 2015
Editada: Star Strider el 13 de Abr. de 2015
Georgina’s first Answer moved here:
Hello Star Strider,
Thank you for your kind reply! I will try to summarize my problem: I have a set of data which corresponds to photon counts over ~ 1000 bins corresponding to 15 min. runs taken by a photodiode I've operated.
While, it is normally understood that the probability distribution is Poisson for photon counts, I'd like to see it that it is so. Thus, given a Poisson distribution such as that mentioned in Matlab under 'definition' associated to poissfit, I wonder 'how' given the data I described above, I could show to myself that the photon counts obtained are indeed of Poisson nature?
Thank you!
Star Strider
Star Strider el 13 de Abr. de 2015
Editada: Star Strider el 13 de Abr. de 2015
Georgina’s second Answer moved here:
BTW, what I mean by 'given a Poisson distribution such as that mentioned in Matlab under 'definition' associated to poissfit, I wonder 'how' given the data I described above' is given what is understood as a Poisson distribution - also described in wiki under definition: http://en.wikipedia.org/wiki/Poisson_distribution
Thank you!
My pleasure!
I’m not sure what you mean by ‘bins’, but I assume they are the numbers of photons counted in each 15 minute observation period. So I would create a vector the photon counts in each of your 1000 15-minute observation periods and use that vector as the data argument to (preferably) histfit, specifying the 'poisson' option, since it will also give you a graphical representation of the distribution of your data.

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