Bayesian Information criterion is independent of prior - What does 'prior' mean here? I am trying to identify the possible number of states in my data. Each state corresponds to a different scenario. For example, I am measuring humidity and trying to identify the number of states in any given day. (Rain, Cloudy, Sunny). 

For this I want to use the BIC to get the optimal number of states when modeling the data with a Gaussian Mixture Model. I am using scikit-learn for this and sklearn documentation says:


  Model selection concerns both the covariance type and the number of components in the model. In that case, AIC also provides the right result (not shown to save time), but BIC is better suited if the problem is to identify the right model. Unlike Bayesian procedures, such inferences are prior-free.


I am not sure what the priors are here.
Thanks in advance!
"Prior" refers to the "already known" probability (or its probability distribution function) of an event happening