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Do samplers or ADVI assume parameters/data points independence? #1517
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I can only answer 1 and 2:
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Actually, it corresponds to a diagonal matrix with entries being equal to |
Hello @cpfiffer @torfjelde @devmotion , And thanks a lot for your support. Specifically answering the 3. reply of @torfjelde , our model is an epidemiological model: you may find an MWE here, where I posted a simplified version of the calibration pipeline, the turing model along with fake ( but credible) data. |
Do you have any update? I would be very interested to know. |
For me, the referenced example is quite long. I find it difficult to know exactly what you're after in the context of your question. But, if you're referring to what prior information you should provide to the model, that would depend on your domain expertise. It seems that your priors are all quite specifically constrained and uniform. If you know there should be some correlation, you would need to reconsider those priors in light of the knowledge you have. If you're asking how one can allow the ADVI algorithm to model correlation between model parameters, then the answer would be to use the full-rank approximation (instead of the default meanfield), which is here as you mentioned. You would not necessarily have to provide domain expertise here, but you do need to ensure the dimensions of the covariance matrix are correct for your model. Thanks for posting this fascinating question here. If you care to continue this discussion, would you reference this in the Discussions page? The Discussions page is meant for questions related to statistical theory and applications. Items related to bugs and feature requests would, of course, still be welcome here in Issues. |
I'm closing this for now as it does not concern bugs or feature requests. As mentioned, feel free to continue this discussion on the Discussions page. |
Hello,
This is a partially theoretical question, I hope it is not too wrong to post it as an issue. These resources:
definitively helped a lot in understanding the inner workings of some of Turing.jl's features, but I'd also like to ask here to be sure.
Suppose one has to calibrate a model ( e.g. a DifferentialEquations.jl model) using Turing.
data ~ MvNormal(predicted, σ)
whereσ
is aFloat64
imply that the points indata
are assumed to be independent?MvNormal(predicted, σ)
with something like aTruncatedMvNormal(predicted, Σ)
( whereΣ
is now a matrix) be the best option? If so, what prior would you set onΣ
( or on its entries)?Thanks in advance
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