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Since `bayestestR` can really be used with any distribution of data, I think it would be good to have a vignette about using it with non-Bayeisan distributions - for example how to use `bayestestR` wi…
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LOO is a common method for evaluating both model fit (LOO-IC analogous to AIC or WAIC) and influential points (pointwise LOO-IC and pareto k LOO-IC evaluation). Methods for both are provided by the *l…
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Both @ericmjl and I are firm believers that Probabilistic Programming has a bright and huge future.
I know other people believe the same. @springcoil has said toe me previously that "PP is the new …
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#### Link: [https://dl.acm.org/doi/10.1145/3589762](https://dl.acm.org/doi/10.1145/3589762)
#### Main problem
Forming successful teams of experts for projects isn't straightforward and complicated. …
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I need to try out different hyperparameters and compare their performance. I would be interested to know if an automated hyperparameter tuning option is available to do a Bayesian optimization as an e…
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As our work on this package progress, this issue can help us enumerate possible future features of the package depending on the time and interests of contributors. Some features will be needed for th…
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Thanks a lot for this toolbox!
I have some issues understanding the methods implemented and understanding why they are performing so differently.
My understanding is as follows:
_method 0_ do…
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# Uncertainty Quantification of ML models: From Introduction to Advanced
# Responsible person(s)
Sebastian Starke, , HZDR,
Steve Schmerler, HZDR, @elcorto
Peter Steinbach, HZDR, @psteinb
G…
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The Bayesian bootstrap simulates an approximation for the posterior distribution of a parameter by assuming that only the observed values are possible, and the relative frequency of each observed valu…
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While the Hyberband and Bayesian tuner are both very good in terms of eventually arriving to a hyperparameter combination, it seems like one of the state of the art hyperparameter tuning methods combi…