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The problem is on the line after the line containing A2 in the file /usr/local/Cellar/findutils/4.7.0/libexec/bin/gupdatedb. Currently, it is of the form:

open can be used when you have a file saved locally on your machine. With pd.read_stata this is not necessary however, as you can specify the file path directly as a parameter.

In this case you want to read in a .dta file from a url so this does not apply. The solution is simple though, as pd.read_stata can read in files from urls directly.

The problem with your first code snippet is that the text you're passing as a parameter to the HTTP call is too long, if you print the response object you'll see:

that corresponds to 414 URI Too Long Reference

If you pass a smaller text, dbpedia-spotlight will be able to annotate the entities for you.

For the second code that you put, you have two problems, the first one is that dbpedia-spotlight may respond with 403 status after consecutive calls to the annotate service, to check that I suggest you to do:

I finally found the answer. It was already in the correct unit - just needed to divide the values by 60 (minutes) and it worked :)

Thanks everyone who tried to help me :)

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It seems to me like in the file "yamlfile_10_5281_zenodo_1745362.yaml" there is a bad token name. Try running it without this file. In python \t is representative of a tab and so cannot be included in a string ect normally without escaping it.

Large covariance matrices can easily be numerically singular, even in this case where they are positive definite by construction. In order to avoid this problem and unnecessary computation, the usual thing to do in Stan is to declare the parameter as the Cholesky factor of a covariance or correlation matrix (in which case you also need to declare a positive vector of standard deviations in the parameters block).

In that case, you would call the multi_normal_cholesky_lpdf function, which conditions on a Cholesky factor of a covariance matrix rather than the covariance matrix itself, which in turn can be a diag_pre_multiply of a vector of standard deviations and the Cholesky factor of a correlation matrix. However, in that case, you would need to switch the priors from an inverse Wishart to a lkj_corr_cholesky_lpdf for the Cholesky factor of the correlation matrix and whatever you want (such as exponential_lpdf) for the standard deviations. This is all discussed in the Stan user manual.

Another option would be to integrate out the covariance matrices.

This website use AJAX to display the result,you can find the AJAX request to get the JSON result.