hi, I am not so clear about the difference bew marignal_likelyhood and prior,in pm.gp module.

take Latent as an example,when shall I use marignal_likelyhood or prior?

Use `marginal_likelihood`

when your observed data is drawn directly from the GP. If you are using the GP as a prior distribution for latent (i.e. unobserved) variables, then use `prior`

. Here’s an example from PyMC v3 which shows how the `prior`

method can be used to create latent RVs. In that example, the observed data are discrete counts (which clearly cannot be drawn from a standard GP) but we use the GP to place a prior over the log-mean value of the Poisson distributed counts.

https://docs.pymc.io/en/v3/pymc-examples/examples/case_studies/log-gaussian-cox-process.html

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thanks,ck.so that means the prior is more general than m_likelyhood?