computing log likelihood via pymc now, there is something going on wi…

…th the bambi version

src/hssm/hssm.py

@@ -592,6 +592,12 @@ def sample(
                     "pymc" if sampler == "mcmc" else sampler.split("_")[1]
                 )
 
+        # Don't compute likelihood directly through pymc sampler
+        compute_likelihood = True
+        if "idata_kwargs" in kwargs:
+            if "log_likelihood" in kwargs["idata_kwargs"]:
+                compute_likelihood = kwargs["idata_kwargs"].pop("log_likelihood", True)
+
         self._inference_obj = self.model.fit(
             inference_method=(
                 "mcmc"
@@ -603,24 +609,9 @@ def sample(
             **kwargs,
         )
 
-        # The parent was previously not part of deterministics --> compute it via
-        # posterior_predictive (works because it acts as the 'mu' parameter
-        # in the GLM as far as bambi is concerned)
-        if self._inference_obj is not None:
-            if self._parent not in self._inference_obj.posterior.data_vars:
-                # self.model.predict(self._inference_obj, kind="mean", inplace=True)
-                # rename 'rt,response_mean' to 'v' so in the traces everything
-                # looks the way it should
-                self._inference_obj.rename_vars(
-                    {"rt,response_mean": self._parent}, inplace=True
-                )
-            elif (
-                self._parent in self._inference_obj.posterior.data_vars
-                and "rt,response_mean" in self._inference_obj.posterior.data_vars
-            ):
-                # drop redundant 'rt,response_mean' variable,
-                # if parent already in posterior
-                del self._inference_obj.posterior["rt,response_mean"]
+        if compute_likelihood:
+            with self.pymc_model:
+                pm.compute_log_likelihood(self._inference_obj)
 
         # Subset data vars in posterior
         if hasattr(self, "pymc_model") and self._inference_obj is not None: