The distribution at epoch n+1 should be the convolution of the distribution at epoch n and your kernel F.Hi,
I am new to this forum and I am not quite sure if my question belongs to this
subforum. Anyway..what I have so far is a so called dispersal kernel of animals.
It is a diffusion curve (like Gaussian) which discribes the spread of an animal
In my special case it is a function of two superimposed normal distributions.
The function gives the distribution of spread after time one time step. The second time step is based on the result (density) of the first time step. So actually on each point of the curve the diffusion is applied again for the second time step and then all curves are added up to get the new distribution. So after each time step the curve will flatten out but will always cover an area of 1 below the curve. Is that clear and correct so far?
Now my question is: How such and equation be written in mathematical terms so that if I have all the curve parameters I can calculate new curves for the time steps I am interested in?
Hopefully someone can help me with that....
PS: Usually I am using Python and Scipy for solving equation etc. in any case if that information is needed....