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Calculate the mean of the entire distribution. #3
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Hi, thanks for a good question. We implement the LogNormMix model in a non-standard way, which changes the formula slightly. The standard way to obtain a LogNormMix is using the following sequence of operations
What we do instead is
The parameters The correct way to compute the mean in this case is Let me know if this works for you. |
@shchur Thank you for your reply. I got it. But I have another question. |
Unfortunately, it's not that simple. In general, |
@shchur OK, I know. Thank you very much! |
Besides, when splitting each sequence into train/val/test, in your code: In addition, what's the purpose of |
That's indeed what happens. Even though RNNs should in theory be able to capture long-range interactions, we found that this additional history lead to absolutely no improvement in performance. So we decided to stick with this version when refactoring the code. This is also consistent with results in other papers (e.g. https://arxiv.org/abs/1905.09690), where they also say that RNNs basically don't learn long-range interactions (or at least no long-range interactions are necessary for TPP models). Also, you can see that our RNN model basically matches the optimal performance on synthetic datasets (like Hawkes), which means that we don't lose much by discarding the history here. This function is necessary when generating new sequences with the RNN. Since the parameters of |
I really appreciate your patience in answering all my questions. |
Thanks for your code.
I wonder how to calculate the mean of the entire distribution.
Because I not only want to get the NLL loss of \tau, but also want to get the rmse/mae of \tau. And I will use the mean of \tau to calculate rmse/mae.
I think I should use the eq E[\tau]=\sum_k w_k \exp( \mu_k + s^2_k/2). But I cannotget the right answer.
Could you tell me how to get the mean? Thank you!
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