diff --git a/src/beanmachine/ppl/inference/monte_carlo_samples.py b/src/beanmachine/ppl/inference/monte_carlo_samples.py
index 8380ced021..2b605f505e 100644
--- a/src/beanmachine/ppl/inference/monte_carlo_samples.py
+++ b/src/beanmachine/ppl/inference/monte_carlo_samples.py
@@ -45,7 +45,10 @@ def __init__(
             self.samples[rv] = val[:, num_adaptive_samples:]
 
         if logll_results is not None:
-            logll = merge_dicts(logll_results, 0, stack_not_cat)
+            if isinstance(logll_results, list):
+                logll = merge_dicts(logll_results, 0, stack_not_cat)
+            else:
+                logll = logll_results
             self.log_likelihoods = {}
             self.adaptive_log_likelihoods = {}
             for rv, val in logll.items():
@@ -92,7 +95,22 @@ def get_chain(self, chain: int = 0) -> "MonteCarloSamples":
             raise IndexError("Please specify a valid chain")
 
         samples = {rv: self.get_variable(rv, True)[[chain]] for rv in self}
-        new_mcs = MonteCarloSamples(samples, self.num_adaptive_samples)
+
+        if self.log_likelihoods is None:
+            logll = None
+        else:
+            logll = {
+                rv: self.get_log_likelihoods(rv, True)[[chain]]
+                for rv in self.log_likelihoods
+            }
+
+        new_mcs = MonteCarloSamples(
+            samples,
+            self.num_adaptive_samples,
+            True,
+            logll,
+            self.observations,
+        )
         new_mcs.single_chain_view = True
 
         return new_mcs
@@ -135,6 +153,30 @@ def get_variable(
             samples = samples.squeeze(0)
         return samples
 
+    def get_log_likelihoods(
+        self,
+        rv: RVIdentifier,
+        include_adapt_steps: bool = False,
+    ) -> torch.Tensor:
+        """
+        :returns: log_likelihoods computed during inference for the specified variable
+        """
+
+        if not isinstance(rv, RVIdentifier):
+            raise TypeError(
+                "The key is required to be a random variable "
+                + f"but is of type {type(rv).__name__}."
+            )
+
+        logll = self.log_likelihoods[rv]
+
+        if include_adapt_steps:
+            logll = torch.cat([self.adaptive_log_likelihoods[rv], logll], dim=1)
+
+        if self.single_chain_view:
+            logll = logll.squeeze(0)
+        return logll
+
     def get(
         self,
         rv: RVIdentifier,