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dfm avatar dfm commented on August 16, 2024

This is an interesting question!

I'd probably consider implementing it using a version of your last proposal, but analytically marginalizing over n. I'd probably fit for a parameter p0 then compute the full model for each value of period = p0 / n for n = .... Then you can just sum over the likelihoods (not the log likelihoods!) to compute the marginalized likelihood.

It might be better to move this conversation to email and we can try to work it out there.

from exoplanet.

dfm avatar dfm commented on August 16, 2024

Here's a demonstration of this method in practice: https://gist.github.com/dfm/122a184be253ead0466c424cf3932b5d

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