As said in the title, what would non-ergodicity mean for Bayesian satistics, and if the process being investigated is non-ergodic, how would Bayesian methods tackle this process - would it be different from ergodic case or not?
So in classical(frequentist) statistic, if the world investigated is stochastic process, in many cases it needs to be ergodic process or needs to converted to ergodic process. I am somehow new to Bayesian statistics, but I know that MCMC method has ergodic theorem. Now, the question is, this is how we can estimate parameters. If so, does this solve many problems related to non-ergodic stochastic process?