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- Name
pyELQ: python Emission Localization and Quantification
- Mission statement
The python Emission Localization and Quantification (pyELQ) code aims to maximize effective use of existing mea…
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Implementation of the empirical Bayesian model.
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Hi, team!
I'm interested in it but it's hard to get it.
I have some questions about wide networks.
1) What's the prediction of wide networks, e.g., NNGP? Evaluated mean of GP? Is it deterministic? If …
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So far, `fatiando.inversion` has a lot of things for finding the least-squares solutions to the inverse problems. However, there is no automated way of estimating the uncertainty of this solution. I'l…
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So during my attempts to run `predict` on very large "testing set" grids, I ran into the classic memory overflow issues that `predict_in_batches` tries to protect against. However, I don't think funct…
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@eecsu @smattis
We would like to add different options for how the observed and predicted distributions are estimated (i.e., with Bayesian GMM) when calculating R in the data-consistent framework. I…
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Is it possible to add the gradient to the location and rotation matrix of the source so that it can be optimized as well?
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By locating the points of change (using `find_inversions`) on all the posterior draws of the link we could have a uni/multi-modal distribution of points of change.
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Description:
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An informed decision is needed regarding which sampler module to use in the training, the old or the new experimental one. Charlie and @amal-ghamdi think it is better to use …
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Following basic matrix manipulations (read: nothing fancy, see e.g. Algorithm 1 in [this source](https://emtiyaz.github.io/Writings/OneColInv.pdf)) the kernel matrix inversion might be maintained in s…