I have a model involving a multinomial likelihood with multiple GPs as my prior (the GPs should have different covariance hyperparameters). I’ve heard that for loops are usually frowned upon when defining a model. Is there a way to define multiple GPs while avoiding a for loop? How ‘bad’ is it if it…

There’s nothing too bad with that loop, you are just automating the creation of 8 separate variables. Loops are generally discouraged in the case where you can use a single variable tensor instead of 100s individual variables, but your case might fall more in the “premature optimization” bucket.

Multiple Gaussian processes without for loop?

Questions

twiecki January 19, 2022, 6:35am 4

You can do multi-dimensional GPs quite easily: Mean and Covariance Functions — PyMC3 3.11.4 documentation

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Multiple Gaussian processes without for loop?

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