Implement earnings-height model in pymc

posterior_database/models/info/earn_height.info.json

@@ -11,6 +11,10 @@
     "stan": {
       "model_code": "models/stan/earn_height.stan",
       "stan_version": ">=2.26.0"
+    }, 
+     "pymc": {
+        "model_code": "models/pymc/earn_height.py",
+        "pymc_version": ">=5.16.2"    
     }
   },
   "references": "gelman2006data",

posterior_database/models/pymc/earn_height.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pymc as pm
+
+def model(data):
+
+    # Define PyMC Model
+    coords = {
+        'obs_idx': np.arange(data["N"]),
+        'feature': ['intercept', 'height']
+    }
+
+    with pm.Model(coords=coords) as earn_height:
+        N = pm.Data("N", data["N"])
+        earn = pm.Data('earn', data['earn'], dims=['obs_idx'])
+        height = pm.Data('height', data['height'], dims=['obs_idx'])
+
+        beta = pm.Flat('beta', dims=['feature'])
+        mu = beta[0] + beta[1] * height
+
+        sigma = pm.HalfFlat('sigma')
+        earn_hat = pm.Normal('earn_hat', mu=mu, sigma=sigma, observed=earn, dims=['obs_idx'])
+
+    return earn_height