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I am trying to implement CReM-A and I was checking your code. I think I am not understanding a small part in the sampling procedure. You implemented the sampling as:
But in the paper, Equation III.14 you have that the weights are distributed according to an exponential distribution with rate beta_i + beta_j and mean 1/(beta_i + beta_j). Which makes sense since then the expected value of the row and column sumn match the observed value, equation III.18.
Is this correct?
And if so one could speed up the code by solving for 1/(beta_i + beta_j) directly by transforming it in a linear problem? I mean by solving a linear equation in terms of rates and not means.
The text was updated successfully, but these errors were encountered:
Hello,
I am trying to implement CReM-A and I was checking your code. I think I am not understanding a small part in the sampling procedure. You implemented the sampling as:
q_ensemble = 1/(beta_i + beta_j) w_link = np.random.exponential(q_ensemble)
therefore you sample from a process with rate
q_ensemble
and mean1/q_ensemble = beta_i + beta_j
as far as I understand the documentation of numpy.random.exponential (https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html)But in the paper, Equation III.14 you have that the weights are distributed according to an exponential distribution with rate
beta_i + beta_j
and mean1/(beta_i + beta_j)
. Which makes sense since then the expected value of the row and column sumn match the observed value, equation III.18.Is this correct?
And if so one could speed up the code by solving for 1/(beta_i + beta_j) directly by transforming it in a linear problem? I mean by solving a linear equation in terms of rates and not means.
The text was updated successfully, but these errors were encountered: