I received this warning when sampling my model:
The rhat statistic is larger than 1.01 for some parameters.
Should I loosen the priors to try and fix this? Here is my model:
with pm.Model() as promo_model:
# Hyperpriors
mu_global = pm.Normal('mu_global', mu=0, sigma=10)
sigma_bl = pm.HalfNormal('sigma_bl', sigma=5)
sigma_statc = pm.HalfNormal('sigma_statc', sigma=5)
sigma_promo = pm.HalfNormal('sigma_promo', sigma=5)
# Group-specific effects
alpha_bl = pm.Normal('alpha_bl', mu=0, sigma=sigma_bl, shape=len(business_line_map))
beta_statc = pm.Normal('beta_statc', mu=0, sigma=sigma_statc, shape=2)
gamma_promo = pm.Normal('gamma_promo', mu=0, sigma=sigma_promo, shape=len(promo_type_map))
# Expected mean
mu = pm.Deterministic(
'mu',
mu_global + alpha_bl[bl_idx_promo] + beta_statc[statc_idx_promo] + gamma_promo[promo_idx_promo]
)
# Likelihood
sigma = pm.HalfNormal('sigma', sigma=10)
nu = pm.Exponential('nu', 1/30)
likelihood = pm.StudentT('likelihood', nu=nu, mu=mu, sigma=sigma, observed=y_promo)
# Posterior sampling
trace_promo = pm.sample(1000, tune=1000, nuts_sampler="numpyro", return_inferencedata=True)