Yet although a similar problem, it is much less in degree: even heavy-tailed distributions are much less sensitive to ‘outliers’ than representors, and orthodox approaches have more resources available to aggregate and judge between a family of precise representations.
Maybe you can go up a level? If you have (at least) two distributions and think one is far more likely than the other (e.g. an outlier), but are unwilling to assign precise probabilities to each distribution, you can use multiple distributions over these distributions. This can reduce the weight of outliers.
Maybe you can go up a level? If you have (at least) two distributions and think one is far more likely than the other (e.g. an outlier), but are unwilling to assign precise probabilities to each distribution, you can use multiple distributions over these distributions. This can reduce the weight of outliers.
I don’t know how practical this would be, though.