I think I found a different version
B[i] =(p(delta[i]=delta[i-1] | y,m[1])/(p(delta[i]=delta[i-1]|m[i])
# Bayes Factor is the propabilty of the different equaling the previous difference knowing y and
m[i], divided by the probabilty of delta[i]=delta[i-1] knowing m[i])
#where y is the dependent variables, and m[i] is a model for each itteration, modifying for what is now known.
I think I have an idea of how a model is constructed in Turing.jl , but I will need to play around with this.