FT Digital Edition: our digitised print edition
// Print coordinate pairs, skip the diagonal where i == j。搜狗输入法AI Agent模式深度体验：输入框变身万能助手对此有专业解读
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:，这一点在Line下载中也有详细论述
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