diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 54029c2d1..30210ce88 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -15,7 +15,8 @@ Release Date: TBD
 ### Major Changes
 
 * Updates the DCEGM tools to address the flaws identified in [issue #1062](https://github.com/econ-ark/HARK/issues/1062). PR: [1100](https://github.com/econ-ark/HARK/pull/1100).
-,
+* Updates `IndexDstn`, introducing the option to use an existing RNG instead of creating a new one, and creating and storing all the conditional distributions at initialization. [1104](https://github.com/econ-ark/HARK/pull/1104)
+
 ### Minor Changes
 
 ### 0.12.0
diff --git a/HARK/distribution.py b/HARK/distribution.py
index b9f7b1512..0615171da 100644
--- a/HARK/distribution.py
+++ b/HARK/distribution.py
@@ -62,26 +62,49 @@ class (such as Bernoulli, LogNormal, etc.) with information
     conditional = None
     engine = None
 
-    def __init__(self, engine, conditional, seed=0):
-        # Set up the RNG
-        super().__init__(seed)
+    def __init__(self, engine, conditional, RNG = None, seed=0):
+        
+        if RNG is None:
+            # Set up the RNG
+            super().__init__(seed)
+        else:
+            # If an RNG is received, use it in whatever state it is in.
+            self.RNG = RNG
+            # The seed will still be set, even if it is not used for the RNG,
+            # for whenever self.reset() is called.
+            # Note that self.reset() will stop using the RNG that was passed
+            # and create a new one.
+            self.seed = seed
 
         self.conditional = conditional
         self.engine = engine
-
-    def __getitem__(self, y):
-        # test one item to determine case handling
+        
+        
+        self.dstns = []
+        
+        # Test one item to determine case handling
         item0 = list(self.conditional.values())[0]
-
+        
         if type(item0) is list:
-            cond = {key: val[y] for (key, val) in self.conditional.items()}
-            return self.engine(seed=self.RNG.randint(0, 2 ** 31 - 1), **cond)
+            # Create and store all the conditional distributions
+            for y in range(len(item0)):
+                cond = {key: val[y] for (key, val) in self.conditional.items()}
+                self.dstns.append(self.engine(seed=self.RNG.randint(0, 2 ** 31 - 1), **cond))
+                
+        elif type(item0) is float:
+            
+            self.dstns = [self.engine(seed=self.RNG.randint(0, 2 ** 31 - 1), **conditional)]
+
         else:
             raise (
                 Exception(
                     f"IndexDistribution: Unhandled case for __getitem__ access. y: {y}; conditional: {self.conditional}"
                 )
             )
+            
+    def __getitem__(self, y):
+        
+        return self.dstns[y]
 
     def approx(self, N, **kwds):
         """
@@ -114,9 +137,7 @@ def approx(self, N, **kwds):
 
         if type(item0) is float:
             # degenerate case. Treat the parameterization as constant.
-            return self.engine(
-                seed=self.RNG.randint(0, 2 ** 31 - 1), **self.conditional
-            ).approx(N, **kwds)
+            return self.dstns[0].approx(N, **kwds)
 
         if type(item0) is list:
             return TimeVaryingDiscreteDistribution(