I am trying to implement a changepoint model. Perhaps a time series example would make it clearer:
Each color is a different segment, where each segment is modeled as a normal distribution with mean zero and a different standard deviation (in the picture, the y-axis are the displacements sampled from such normal distribution for the segment). The number of segments is arbitrary.
I implemented a model for this using maximum likelihood based on literature specific for my problem. However, I need to add a bit more complexity to the model so I want to move on to a Bayesian framework.
In the past, I’ve implemented change points using reversible-jump mcmc. I don’t think this is possible with Stan correct? Any suggestions?