Hi @Jacques, this is strange. I was a bit worried that this is the problem of your model, where you use MCMC to sample forecast values. There is a similar thread (see also my comment there) which discussed the instability when we switch the order of the variables in the model. I don’t think that the model
def model(data):
sigma = sample('x', Exponential(0, 1))
sample('data', Normal(0, sigma), obs=data)
sample('forecast', Normal(0, sigma), obs=None)
will give something useful. For that model, forecast will concentrate around 0. Maximizing probability of forecast will lead to small sigma. But small sigma will lead to small probability of data…
What happens if you just use MCMC to get posterior samples, rather than to get forecasting values?