new

aemcmc/conjugates.py

@@ -8,7 +8,7 @@
 conjugatesdb = OptimizationDatabase()
 
 
-def beta_binomial_conjugateo(observation_model_expr, value_expr, posterior_expr):
+def beta_binomial_conjugateo(observed_rv_expr, posterior_expr):
     r"""Produce a goal that represents the application of Bayes theorem
     for a beta prior with a binomial observation model.
 
@@ -25,6 +25,15 @@ def beta_binomial_conjugateo(observation_model_expr, value_expr, posterior_expr)
 
         p \sim \operatorname{Beta}\left(\alpha + Y, \beta + n - Y\right)
 
+    Parameters
+    ----------
+    observed_rv_expr
+        A tuple that contains expressions that represent the observed variable
+        and it observed value respectively.
+    posterior_exp
+        An expression that represents the posterior distribution of the latent
+        variable.
+
     """
 
     # Beta-binomial observation model
@@ -55,8 +64,8 @@ def beta_binomial_conjugateo(observation_model_expr, value_expr, posterior_expr)
     )
 
     return lall(
-        eq(observation_model_expr, Y_et),
-        eq(value_expr, y_lv),
+        eq(observed_rv_expr[0], Y_et),
+        eq(observed_rv_expr[1], y_lv),
         eq(posterior_expr, p_posterior_et),
     )
 

tests/test_conjugates.py

@@ -26,7 +26,7 @@ def test_beta_binomial_conjugate_contract():
     y_vv.tag.name = "y"
 
     q_lv = var()
-    (posterior_expr,) = run(1, q_lv, beta_binomial_conjugateo(Y_rv, y_vv, q_lv))
+    (posterior_expr,) = run(1, q_lv, beta_binomial_conjugateo((Y_rv, y_vv), q_lv))
     posterior = eval_if_etuple(posterior_expr)
     aesara.dprint(posterior)