# How is MAP 'not invariant to reparametrization'? [duplicate]

I was watching a lecture on coursera on 'Bayesian Methods on Machine Learning' and I came across a statement that: MAP(Maximum a posteriori) is not invariant to reparametrization. I didn't quite understand:

1. What does reparametrization mean here and why is reparametrization important?

2. How is MAP 'not invariant to reparametrization'?

3. Why 'not being invariant to reparametrization' a problem and How do Conjugate priors help solve this problem?

Please explain the answers in an easy and intuitive way. I looked for other similar questions to mine and they are way too mathematical and I don not have a solid theoretical foundation in bayesian statistics.

PS: Please do not mark this question as a duplicate. I have read other similar questions on StackOverflow and other sites, however, those questions don't answer my questions completely and clearly.

Edit: Even after writing a special note that I have viewed every possible question similar to this, and those do not answer my questions, This question was marked duplicate.