Comments (4)
You are correct that log_prob()
computes log-intensity plus log survival probability (i.e., log intensity minus the integral). In the code (lines 128-130) I only compute the log survival probability for the time interval [t_N, T)
, i.e. between the last event and the end of the observed time interval. Does this answer your question?
from ifl-tpp.
Thanks. However, in lots of implementation, the final time interval survival probability is omitted, and do you agree that this can be all omitted, which will still be a strict model comparison.
from ifl-tpp.
I would be very careful when comparing to other implementations. Small changes (like rescaling / normalizing the times) can totally change the log-likelihood and make the results incomparable. I have also seen some implementations that compute the loss differently, like only computing the log-likelihood for marks, adding some regularization terms, or sometime even having serious bugs in the computation.
from ifl-tpp.
Thanks a lot. I will continue following and expanding your fantastic work!
from ifl-tpp.
Related Issues (20)
- LogNorm curiosity HOT 4
- Code for using context vector in the models HOT 2
- Loss with NLL of mark and MAE of inter-event time HOT 6
- history HOT 5
- Hyperparameters for reproducibility HOT 6
- Sampling points of a specific mark HOT 3
- Implementation on missing data imputation HOT 1
- Understanding given datasets HOT 5
- NLL results HOT 5
- Sampling with additional conditional information
- use other dataset HOT 1
- Missing data imputation HOT 1
- Calculate the mean of the entire distribution. HOT 7
- all evaluation expriments code HOT 1
- Calculate the mean of the entire distribution. HOT 23
- How could I get the predicted results? HOT 20
- ATM dataset testing HOT 2
- Learning with Marks HOT 9
- How to get the expression of the distribution of inter-event time HOT 6
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