Is it possible to add iterations to a fitted stan model that did not converge - without running the entire model?
For example, in JAGS one can call update on a model fit to run additional warmup iterations if additional warmup is needed, or one can call jags.samples to get new samples if additional samples are needed (generic code below). This helps to reduce runtime as one doesn’t have to start over.
mymod <- jags.model(file = ‘myfile.txt’, data = XX, …)
update(mymod, n.iter = 100)
mysamps <- coda.samples(mymod, n.iter = 1000)
- Look at convergence - effecive sample size low or parameters are not stable
- Update the model further and get new samples (starts at iteration 1101)
update(mymod, n.iter = 500)
mysamps2 <- coda.samples(mymod, n.iter = 2000, thin = 3)
Is this possible in STAN using rstan?
My hour of searching has turned up nothing - and all documents suggest that one has to run the entire model again. With large models on big data that take hours to run - this is a downside.