Originally Posted by

**Bekos**
Things are a bit more clear now. I have manage to figure out how to implement Random-Walk M-H and Independent M-H. Turns out that the proposal ratio of a gaussian in the independent M-H does not have to be equal to 1. I haven't figure out how to implement the Vanilla M-H though. The algorithms says that at the start of each iteration I have to "draw a sample from the proposal distribution taking into consideration the current sample". All the implementations I found on the internet do the following: They re-center the gaussian proposal at the current sample and then they randomly draw a sample. This turns out to be a Random-Walk M-H. I can not figure out how I can implement an M-H with a gaussian proposal distribution without making it Random-Walk!