Thank you @junpenglao.
I changed it to this based on your recommendation:
with pm.Model() as sales_model:
#define the priors alpha = pm.Normal('intercept', mu=train['Weekly_Sales'].mean(), sd = train['Weekly_Sales'].std()) beta_1 = pm.Normal('dept', mu = 0, sd = 10, shape = X_train['Dept'].shape) beta_2 = pm.Normal('IsHoliday_T', mu = 0, sd = 10, shape = X_train['IsHoliday_True'].shape) #beta_3 = pm.Normal('Week', mu=0, sd = 10) #beta_4 = pm.Normal('Fuel_Prices', mu=0, sd = 10) #beta_5 = pm.Normal('Temperature', mu=0, sd = 10) #beta_6 = pm.Normal('Markdown1', mu=0, sd = 10) #beta_7 = pm.Normal('Markdown2', mu=0, sd = 10) #beta_8 = pm.Normal('Markdown4', mu=0, sd = 10) #beta_9 = pm.Normal('Markdown5', mu=0, sd = 10) #beta_10 = pm.Normal('CPI', mu=0, sd = 10) #beta_11 = pm.Normal('Unemployment', mu=0, sd = 10) s = pm.Uniform('sd', lower = 1, upper = 20) #define the likelihood mu = alpha + beta_1*X_train['Dept'].values + beta_2*X_train['IsHoliday_True'].values #+ beta_3*X_train['Week'] + beta_4*X_train['Fuel_Price_s'] + beta_5*X_train['Temperature_s'] + beta_6*X_train['MarkDown1_s'] + beta_7*X_train['MarkDown2_s'] + beta_8*X_train['MarkDown4_s'] +beta_9*X_train['MarkDown5_s'] + beta_10*X_train['CPI_s'] + beta_11*X_train['Unemployment_s'] y = pm.Normal('sales', mu = mu, sd = s, observed = Y_train, shape = Y_train.shape) trace = pm.sample(draws=10000 ,progressbar=True)
Now the following error shows up.
RuntimeError: Chain 0 failed.