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This week was all about trying to wrangle PyTensor to correctly marginalise out the hyperparameters whilst leaving \(x\) untouched by pm.sample. In contrast to prior weeks, there was much less focus on the theory, as this time it was a matter of trying to get PyMC and PyTensor to do what I want.
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This week I got started on my next PR to implement “regular” (i.e. non-Laplace) marginalisation on the hyperparameters, and also develop a skeleton for a user-facing fit_INLA method which would essentially be the final product.
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This week I was finally able to get my first INLA-related PR merged in!
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My PR’s almost done - I promise!!
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Over this past week, I’ve been working to get this issue closed out. INLA allows us to perform Bayesian inference over models with latent Gaussians. By “latent”, we mean that the Gaussian component is not what we directly observe, but is related to the observed data nonetheless.
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Hi there! This is the first in my blog series documenting my Google Summer of Code 2025 project. Every week (or fortnight, depending on my time), I’ll be posting a brief update to document my progress and share my thoughts on my work.