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pymc-devs / pymc-examples

Examples of PyMC models, including a library of Jupyter notebooks.
https://www.pymc.io/projects/examples/en/latest/
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Improve Kronecker example #653

Closed AlexAndorra closed 1 month ago

AlexAndorra commented 3 months ago

Various updates and improvements on the Kronecker variance GP example:

📚 Documentation preview 📚: https://pymc-examples--653.org.readthedocs.build/en/653/

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AlexAndorra commented 1 month ago

@bwengals can we merge this one please?

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bwengals commented on 2024-05-27T05:04:43Z ----------------------------------------------------------------

The truncated normal makes sense to me for the lengthscales, but it seems a bit strange for eta and sigma, because it won't let either the GP or the noise go to zero. I think it'd make more sense to use HalfNormals or something like that for those parameters. Does it still sample with those priors?


AlexAndorra commented on 2024-05-27T18:25:51Z ----------------------------------------------------------------

Yep, it still samples with pm.HalfNormal("sigma", 0.5) and pm.HalfNormal("eta", 0.5)

But eta and sigma can't be equal to zero anyways, can they? What I'm trying to do is avoid the near-zero region. Does that make sense?

bwengals commented on 2024-05-27T22:32:07Z ----------------------------------------------------------------

I think its analogous to doing a linear regression, and putting a Normal(0, sigma) prior on the beta coefficients, and a half-normal or something on the likelihood sigma, right? Or Im missing something.

Like it'd be strange to do y ~ N(beta0 + beta1 * x, sigma) with truncated normal priors on beta0, beta1 and sigma? Unless you had a very particular reason

AlexAndorra commented on 2024-05-28T13:40:24Z ----------------------------------------------------------------

Yep, agreed. I've definitely done the second case -- it's very useful when you have prior information about the parameters. Here though it's hard to argue for or against, as it's a simulated case.

Anyways, that's not a blocker and runs fine now, so we can go ahead

bwengals commented 1 month ago

Ah sorry for missing this one, left one quick Q then all good.

AlexAndorra commented 1 month ago

Yep, it still samples with pm.HalfNormal("sigma", 0.5) and pm.HalfNormal("eta", 0.5)

But eta and sigma can't be equal to zero anyways, can they? What I'm trying to do is avoid the near-zero region. Does that make sense?

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AlexAndorra commented 1 month ago

Thanks @bwengals ! Just pushed the changes. I also had a small question on your comment, but this is not a blocker, so feel free to merge

bwengals commented 1 month ago

I think its analogous to doing a linear regression, and putting a Normal(0, sigma) prior on the beta coefficients, and a half-normal or something on the likelihood sigma, right? Or Im missing something.

Like it'd be strange to do y ~ N(beta0 + beta1 * x, sigma) with truncated normal priors on beta0, beta1 and sigma? Unless you had a very particular reason

View entire conversation on ReviewNB

AlexAndorra commented 1 month ago

Can you approve and merge @bwengals if that looks good to you?

AlexAndorra commented 1 month ago

Yep, agreed. I've definitely done the second case -- it's very useful when you have prior information about the parameters. Here though it's hard to argue for or against, as it's a simulated case.

Anyways, that's not a blocker and runs fine now, so we can go ahead

View entire conversation on ReviewNB

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