Model comparison and robustness

I’m still relatively new to pymc. When using or arviz.loo for the pymc gaussian process model I get the warning:

Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.

What exactly would constitute a more robust model here. Any help is appreciated. Thank you.

I had this same question…a good link was sent to me. See the link in this post: What does it mean to have a pareto distribution with a shape parameter greater than .7? - Questions / version agnostic - PyMC Discourse

Thanks for the link!