Is it possible to use the technique below, but for autoregressive priors on parameters (as opposed to data)? Maybe not because they wouldn’t be initialized…?
What is a more efficient way to code the below, which gives the warning Info: left-hand side variable (name=gamma) occurs on right-hand side of assignment, causing inefficient deep copy to avoid aliasing. ?
for(i in 1:I)
for(t in 2:T)
gamma[i,t]=gamma[i,t-1]+gamma_raw[t,i]*sigma_region_time[gg[i]];
If you have temporal data, it would be better to make y_lag or whatever in the transformed data block rather than slicing every leapfrog step. For parameters …