Thanks for your reply. I have a feature k and the importance of the feature differs depending on the timestep t. I want to model this varying importance with an alpha \alpha \sim N for every timestep. An example of this can be seen in the picture I provided. The x values are the timesteps from 0 to 99 and the y values are the fitted posterior values for each alpha. What I don’t like about this picture are the jumps / or the really high changes of the alpha value. From 0 to 1 it goes from -200 to 150 for the importance of this feature. This just doesn’t make any sense to me. If the feature is not important in t=0 it can’t / shouldn’t be that important in t=1. Therefore, I would like to make the different alphas kind of dependent. The first idea was to model alpha as \alpha = N(\alpha_{t-1}, 2) but I am not sure how / of that is possible.
I hope that makes my problem more clear.