Dear Junpeng,
in R’s “MCMCgllm” toolbox there is one argument “degree of belief parameter (nu)” in some prior configurations.
Taken from one of the tutorials in this series, I had remembered that (at least in analytical Bayesian calculations) the posterior is kind of a “mixture” (plain average or with mixing ratios) of prior and data. A higher amount of data pulls the posterior more towards it, but this could also be achieved by weighting the prior down (low “degree of belief” in my prior).
For example, I could just duplicate my data set (count each observation twice) and should get narrower posteriors. Right?
Falk