For the problems I work on, I’m commonly fitting parameters to 50-100 individual data sets for multiple different models. To compare them, I might use something like a DIC calculation which requires the loglikelihood. Since my loglikelihood function takes ~1ms to run, I need to use a fast sampler like DeMetropolisZ, which requires ~50,000 samples to get good results back.
The end result of this is that it takes minutes to just compute my loglikelihood during parameter inference. Since PyMC isn’t saving the loglikelihood during parameter inference, I have to recalculate it later, which can take hours for all the data I’m working with. If I could save the loglikelihood values while it’s sampling (without having to calculate twice as Deterministic is doing), the problem goes away.