I am in the middle of finishing my undergraduate thesis which titled "Model Selection Analysis with Bayesian Model Averaging on Logistic Regression". There are some questions that I want to ask you about this BMA topics. And I will really appreciate it if you want to answer my questions. Here is the questions:
Can you give me any reference that explain the derivation of BIC approximation that based on bic.glm package? I have read reference about approximation of marginal likelihood and it can be approximated with exp(1/2 BIC), because of the BIC formula doesn't contain any information about prior distribution of parameter, then do we still need the specification of prior parameter or not? and is the bic.glm package use this marginal likelihood approximation too for computing the posterior probability of model?
The problems that i face right now is i want to analyze medical data about low-birth weight born babies, but i don't have any prior information or prior beliefs about the data. so i am not sure which prior distribution should i use for the model parameters. is using noninformative prior appropriate for the BMA analysis? or is there any suggestion which prior that seems reasonable for my case? if i have to used non-informative prior then is it okay to give the prior parameter by 0,5 for all parameter like what has been given in the default of statistical package? thank you for your kind of help. i do really appreciate it
That is the questions that I hope you to answer. Thank you for your attention for reading this, and I will really appreciate it if you answer the questions.