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Feature parity has been reached, and as such I believe enough features are in place for the code to be usable. We just need to ensure the code does the correct thing. For this reason I want to wait for a little until 1.0. For the time being I will release a new pre-release version for testing.
Perhaps some simple models in a python script and a rust example, then compare the confidence intervals? Are there intro to bays with emcee books/blogs, if so we could port the examples and insure we get the same result?
Absolutely, I'm interested in testing the parity with the "reference" Python version as much as possible. I have very little time unfortunately at the moment, and I'm no longer a user of emcee myself so I am a little out of touch with this library (and the Python implementation)
Any feedback or input you can provide would be really valuable for me.
Feature parity has been reached, and as such I believe enough features are in place for the code to be usable. We just need to ensure the code does the correct thing. For this reason I want to wait for a little until 1.0. For the time being I will release a new pre-release version for testing.
Depends on:
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