Hi, I am new to pymc3. I am trying to implement an unpooled model with 3 random variables (b0,b1,b2 ~ Normal) instead of one (b) with shape= 3. The code is from the book Bayesian Modeling and Computation in Python. Understanding how to do this it will help me implement a more complex multilevel model afterwards. I don’t know how to pass the three variables to the deterministic variable.
customers = sales_df.loc[:, "customers"].values sales_observed = sales_df.loc[:, "sales"].values food_category = pd.Categorical(sales_df["Food_Category"]) with pm.Model() as model_sales_unpooled: s = pm.HalfNormal("s", 20, shape=3) b = pm.Normal("b", mu=10, sigma=10, shape=3) m = pm.Deterministic("m", b[food_category.codes] *customers) sales = pm.Normal("sales", mu=m, sigma=s[food_category.codes], observed=sales_observed)