# Validation of inverse problem solution based on Bayesian method

Recently, I read a paper about the inverse problem and parameter estimation. The main approach of the paper is based on the Bayesian method. The answer in this method is a posterior probability density function.

Part of the paper describes: "The validation of obtained results is important. We have to verify that they make sense and don't simply trust our results."

There is no more information due to results validation is out of paper scope. I searched and found some articles. But the math of articles is above my knowledge.

Can someone illustrate the results validation concept in a simple way?

I want to know simply what it said and how it works.

Now imagine that you are testing a physical process, and the dense region of your estimate for some parameter is $$9.1<\mu_x<9.9.$$ You know that if $$\mu_x\approx{9.1}$$, or greater, then the object you are using should tear apart because the tensile strength of the materials wouldn't handle that. Nonetheless, the object you are testing is in good shape.