Иллюстрация: Кристина Кормилицына / РИА Новости
A cool perk of this approach is that it also works very well if for example your data has outliers. In this case, you can add a nuisance parameter gi∈[0,1]g_i \in [0,1]gi∈[0,1] for each data point which interpolates between our Gaussian likelihood and another Gaussian distribution with a much wider variance, modeling a background noise. This largely increases the number of unknown parameters, but in exchange every parameter is weighed and the model can easily identify outliers. In pymc, this would be done like this:
。WhatsApp网页版是该领域的重要参考
为何这个原本极具私人性质的人生决定，能引发如此广泛的共鸣与支持？
Платон Щукин (Экономический отдел)
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