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Hello! We are processing a dataset with hidden state numerical values and additional features like distance and velocity. When we run the dataset with just the numerical values, the model works but wh…
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### Describe the workflow you want to enable
Scikit-learn provides multiple covariance estimators useful for different purposes. Heteroskedastacity-Aware Estimators are designed for correcting bias d…
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Good morning, I've a quick question. Starting from a database is it possible to get a text/csv file with the covariance (linear/angular) of all nodes? I've seen there's the possibility to plot it with…
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Hi, may I ask what's the difference between the three Reg/Strategy? Is ICP-Reg only lidar or is always based on visual features?
I'm currently using proximity detection + non robust loop closures a…
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Is there any interest in supporting covariance matrices between different spectral elements? In `sncosmo`, we're looking at how we can fit models of supernovae to spectral data, and are interested in …
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there is a ValueError when I try to run the plot_em.py example. I use python 3.7 with numpy version 1.20.3 and scipy 1.6.3.
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I ran 50000 steps with the Planck-2018 TT data firstly without a covariance matrix and got an abysmal acceptance rate of ~0.01.
During this run, it often gave this message ("failed to calculate covar…
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# 🐛 Bug
Calls to _diagnonal() error when keops kernels are used. This showed up when tring to use keops kernels in a Multitask/Multi-output approximate GP with SVI. But below is a MWE of the failur…
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I would like to be able to run a GEE analysis using an unstructured covariance. In R this is very easy to do, e.g:
```
library(geepack)
geeglm(
outcome ~ . -cluster_id,
id = cluster_id,
…
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Just wanted to park your gist here since I came across it this week, looking for similar. I don't know if it's something we want to include or if it belongs in scipy or scikit-learn (I don't think it'…