Solved – the difference between a mixture model and a hierarchical model

What is the difference between mixture and hierarchical models?

Are they of the same nature with different names or they are totally different things?

If there are any references, I will be happy to know.

Terminology is not as standardized as one may whish, so what I understand under these terms may not be what others understand under these terms.

I understand under a mixture model a model that posits that posits a mixture of "brand name distributions" for the dependent variable. For example a discrete mixture of normals assumes that a person is drawn with a to be estimated probability from one normal distribution and with another probability form another normal distribution, etc., for a given number of groups. Notice that in this model we do not know which person belongs to which group

Under a hierarchical model I understand that we have observations that are nested in groups. For example: students are nested in classrooms, which are nested in schools, which are nested in countries. Here membership of the groups is known and part of the data.

Similar Posts:

Rate this post

Leave a Comment