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We discussed this briefly at lunch, yesterday; i have some thoughts but not time to put them down now. just wanted to document it.
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Original ticket http://projects.scipy.org/numpy/ticket/1223
Reported 2009-09-15 by trac user zero79, assigned to unknown.
numpy.random.multivariate_normal will happily accept non-physical covariance …
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In this new paper, the authors argue that the diffusion parameter (output scale in probdiff) should be set on a decreasing schedule (rather than learned in the case of Fenrir).
Diffusion Tempering …
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Implement one or more forms of the matrix normal distribution. The general distribution is of the form
```
matrix_normal(matrix[N, P] y | matrix[N, P] mu, cov_matrix[N, N] SigmaRow, cov_matrix[P,…
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The K-Means implementation is a hack -- it's just EM with small, isotropic covariance matrices. Need to do an honest-to-goodness K-Means implementation.
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Bug bounty logistic details (click to expand)
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we should add tools for fitting and stats that are able to handle correlated errors and covariance in fitting (in models?) and stats, and error propogation.
from inconference on astropy.stats at py…
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Take as input (assumed clean-ish) .nii or .nii.gz files, and make correlation/covariance matrices. See fmri_to_matrix and timeseries_to_matrix
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An issue that @NPounder identified regarding memory use...
The prior inverse covariance is defined as a dense matrix, so it does use far too much memory. The situation gets worse in the Hessian calc…
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It seems that RevBayes doesn't implement matrix algebra such as sum or product of two matrices:
```
A