notebooks

docs/source/notebooks/1.1-Binary_HGF.md

@@ -208,11 +208,11 @@ slideshow:
 with pm.Model() as two_levels_binary_hgf:
 
     # Set a prior over the evolution rate at the second level.
-    omega_2 = pm.Uniform("omega_2", -3.5, 0.0)
+    tonic_volatility_2 = pm.Uniform("tonic_volatility_2", -3.5, 0.0)
 
     # Call the pre-parametrized HGF distribution here.
     # All parameters are set to their default value, except omega_2.
-    pm.Potential("hgf_loglike", hgf_logp_op(omega_2=omega_2))
+    pm.Potential("hgf_loglike", hgf_logp_op(tonic_volatility_2=tonic_volatility_2))
 ```
 
 #### Visualizing the model
@@ -229,15 +229,15 @@ with two_levels_binary_hgf:
 ```
 
 ```{code-cell} ipython3
-az.plot_trace(two_level_hgf_idata, var_names=["omega_2"]);
+az.plot_trace(two_level_hgf_idata, var_names=["tonic_volatility_2"]);
 plt.tight_layout()
 ```
 
 ### Using the learned parameters
 To visualize how the model would behave under the most probable values, we average the $\omega_{2}$ samples and use this value in a new model.
 
 ```{code-cell} ipython3
-omega_2 = az.summary(two_level_hgf_idata)["mean"]["omega_2"]
+tonic_volatility_2 = az.summary(two_level_hgf_idata)["mean"]["tonic_volatility_2"]
 ```
 
 ```{code-cell} ipython3
@@ -246,7 +246,7 @@ hgf_mcmc = HGF(
     model_type="binary",
     initial_mean={"1": jnp.inf, "2": 0.5},
     initial_precision={"1": 0.0, "2": 1.0},
-    tonic_volatility={"1": jnp.inf, "2": omega_2},
+    tonic_volatility={"1": jnp.inf, "2": tonic_volatility_2},
     tonic_drift={"1": 0.0, "2": 0.0},
     volatility_coupling={"1": 1.0}).input_data(
         input_data=u
@@ -287,12 +287,17 @@ slideshow:
 with pm.Model() as three_levels_binary_hgf:
 
      # Set a prior over the evolution rate at the second and third levels.
-    omega_2 = pm.Uniform("omega_2", -4.0, 0.0)
-    omega_3 = pm.Normal("omega_3", -11.0, 2)
+    tonic_volatility_2 = pm.Uniform("tonic_volatility_2", -4.0, 0.0)
+    tonic_volatility_3 = pm.Normal("tonic_volatility_3", -11.0, 2)
 
     # Call the pre-parametrized HGF distribution here.
     # All parameters are set to their default value except omega_2 and omega_3.
-    pm.Potential("hgf_loglike", hgf_logp_op(omega_2=omega_2, omega_3=omega_3))
+    pm.Potential(
+        "hgf_loglike", 
+        hgf_logp_op(
+            tonic_volatility_2=tonic_volatility_2, 
+            tonic_volatility_3=tonic_volatility_3)
+    )
 ```
 
 #### Visualizing the model
@@ -314,16 +319,16 @@ with three_levels_binary_hgf:
 ```
 
 ```{code-cell} ipython3
-az.plot_trace(three_level_hgf_idata, var_names=["omega_2", "omega_3"]);
+az.plot_trace(three_level_hgf_idata, var_names=["tonic_volatility_2", "tonic_volatility_3"]);
 plt.tight_layout()
 ```
 
 ### Using the learned parameters
 To visualize how the model would behave under the most probable values, we average the $\omega_{2}$ samples and use this value in a new model.
 
 ```{code-cell} ipython3
-omega_2 = az.summary(three_level_hgf_idata)["mean"]["omega_2"]
-omega_3 = az.summary(three_level_hgf_idata)["mean"]["omega_3"]
+tonic_volatility_2 = az.summary(three_level_hgf_idata)["mean"]["tonic_volatility_2"]
+tonic_volatility_3 = az.summary(three_level_hgf_idata)["mean"]["tonic_volatility_3"]
 ```
 
 ```{code-cell} ipython3
@@ -332,7 +337,7 @@ hgf_mcmc = HGF(
     model_type="binary",
     initial_mean={"1": jnp.inf, "2": 0.5, "3": 0.0},
     initial_precision={"1": 0.0, "2": 1e4, "3": 1e1},
-    tonic_volatility={"1": jnp.inf, "2": omega_2, "3": omega_3},
+    tonic_volatility={"1": jnp.inf, "2": tonic_volatility_2, "3": tonic_volatility_3},
     tonic_drift={"1": 0.0, "2": 0.0, "3": 0.0},
     volatility_coupling={"1": 1.0, "2": 1.0}).input_data(
         input_data=u