Background:

I am trying to model the rate function for my own dataset between 0-24h time window. However, I have several days of data and would like to use one function to capture the variance of all of them.

Question:

I found the Cox process tutorial on the GPytorch website here to be exactly what I was looking for.

However in the example they fit the function base on one sample which they create synthetically at the start of the tutorial.

In my case I would have something like this (and would want to train one function for it):

Current hack:

My current solution to this is to just loop over every sequence sample.

`for i in range(num_iter): for sequence in dataset: # this loop not here in tutorial loss = infer.step(sequence, quadrature_times)`

Question:

- Even if this is inefficient, is this still addressing the problem correctly? As in, the resultant mean and variances are mathematically correct for my goal?
- How would you do this properly with batching?