Turing.jl: unlikely posterior with bayesian differential equation?

In that case, it seems to me the prior is at odds with your own observations. If I take it correctly, the patients from the study did not show such large bleeding rates. Then again, a study is only a limited sample. Had they included more patients, chances are, you’d see a much broader distribution for \eta.

Maybe patients cluster into low- and high-frequency bleeders and you could use a mixture model, i.e. different priors for each category?

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