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The noise covariance matrices for Russian and English dataset seem to have different structure - the entries dedicated to the EEG and MEG parts are different. How is that information reflected in the …
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### What is the problem this feature will solve?
Read/write covariance matrices to FITS files.
### Describe the desired outcome
I am able to write a covariance matrix out to a FITS file an…
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Methods like calling TJPCov and using the internal gaussian seem to be quite unstable leading to non-positive definite matrices and breaking the cholesky decomposition.
I haven't yet found a way to…
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`insight` has a [helper function](https://github.com/easystats/insight/blob/026b973242a39831bd7b8731475086bbd6787a0a/R/helper_functions.R#L287) that claims to check singularity of mixed effect models.…
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Just found that on the latest version pyspi==1.1.0 (google colab) and a previous version pyspi==1.0.3 (ubuntu)
1) diagonal entries of calculated covariance matrices like that returned by cov_Emp…
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I have the following data:
```
ds unique_id y
0 2003-01-01 Bigeye Tuna 1561.149362
1 2003-01-01 Bluefin Tuna 9619.000000
2 2003-01-01 Southern Bluefin Tuna 3656.876812
3 20…
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So, I understand that you are learning the inverse covariance matrix. You do so by defining a completely trainable matrix `X`, and then you obtain the inverse of the covariance matrix as `S = X @ X.T`…
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I think this is all I need to do:
```
# Covariance matrix under an OU model
base.covar = vcv(rescale(tre2, sigsq = 1, alpha = 0.1, model = "OU"))
# Add squared standard error to the diagonal
covar = …
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For large datasets, the inversion of the covariance matrix and calculation of the likelihood will be a major bottleneck if we continue with the current naive (_O(n^3)_) implementation. This will becom…
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Hi,
I wanted to know if there is a specific reason why, in asr_calibrate, the following loop was used for the covariance estimation
line 158
```
% calculate the sample covariance matrices U (…