diff --git a/pyciemss/mira_integration/distributions.py b/pyciemss/mira_integration/distributions.py
index 1e0b645d..3fa830cc 100644
--- a/pyciemss/mira_integration/distributions.py
+++ b/pyciemss/mira_integration/distributions.py
@@ -1,20 +1,20 @@
-from typing import Callable, Dict, Union
+import warnings
+from typing import Dict
 
 import mira.metamodel
 import pyro
+import torch
 
+ParameterDict = Dict[str, torch.Tensor]
 
-def mira_uniform_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+
+def mira_uniform_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     low = parameters["minimum"]
     high = parameters["maximum"]
     return pyro.distributions.Uniform(low=low, high=high)
 
 
-def mira_normal_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_normal_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     if "mean" in parameters.keys():
         loc = parameters["mean"]
     if "stdev" in parameters.keys():
@@ -28,7 +28,7 @@ def mira_normal_to_pyro(
 
 
 def mira_lognormal_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     if "meanLog" in parameters.keys():
         loc = parameters["meanLog"]
@@ -42,7 +42,7 @@ def mira_lognormal_to_pyro(
 
 # Provide either probs or logits, not both
 def mira_bernoulli_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     if "probability" in parameters.keys():
         probs = parameters["probability"]
@@ -54,12 +54,12 @@ def mira_bernoulli_to_pyro(
     return pyro.distributions.Bernoulli(probs=probs, logits=logits)
 
 
-def mira_beta_to_pyro(parameters: Dict[str, float]) -> pyro.distributions.Distribution:
+def mira_beta_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     return pyro.distributions.Beta(alpha=parameters["alpha"], beta=parameters["beta"])
 
 
 def mira_betabinomial_to_pyro(
-    parameters: Dict[str, Union[float, list]]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     raise NotImplementedError(
         "Conversion from MIRA BetaBinomial distribution to Pyro distribution is not implemented."
@@ -73,9 +73,7 @@ def mira_betabinomial_to_pyro(
     )
 
 
-def mira_binomial_to_pyro(
-    parameters: Dict[str, Union[float, list]]
-) -> pyro.distributions.Distribution:
+def mira_binomial_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     total_count = parameters["numberOfTrials"]
     if "probability" in parameters.keys():
         probs = parameters["probability"]
@@ -89,30 +87,28 @@ def mira_binomial_to_pyro(
     )
 
 
-def mira_cauchy_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_cauchy_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     loc = parameters["location"]
     scale = parameters["scale"]
     return pyro.distributions.Cauchy(loc=loc, scale=scale)
 
 
 def mira_chisquared_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     df = parameters["degreesOfFreedom"]
     return pyro.distributions.Chi2(df=df)
 
 
 def mira_dirichlet_to_pyro(
-    parameters: Dict[str, list]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     concentration = parameters["concentration"]
     return pyro.distributions.Dirichlet(concentration=concentration)
 
 
 def mira_exponential_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     if "rate" in parameters.keys():
         rate = parameters["rate"]
@@ -121,7 +117,7 @@ def mira_exponential_to_pyro(
     return pyro.distributions.Exponential(rate=rate)
 
 
-def mira_gamma_to_pyro(parameters: Dict[str, float]) -> pyro.distributions.Distribution:
+def mira_gamma_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     if "shape" in parameters.keys():
         concentration = parameters["shape"]
     if "scale" in parameters.keys():
@@ -132,7 +128,7 @@ def mira_gamma_to_pyro(parameters: Dict[str, float]) -> pyro.distributions.Distr
 
 
 def mira_inversegamma_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
     raise NotImplementedError(
         "Conversion from MIRA InverseGamma distribution to Pyro distribution is not implemented."
@@ -140,17 +136,13 @@ def mira_inversegamma_to_pyro(
     # TODO: Map parameters to Pyro distribution parameters
 
 
-def mira_gumbel_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_gumbel_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     loc = parameters["location"]
     scale = parameters["scale"]
     return pyro.distributions.Gumbel(loc=loc, scale=scale)
 
 
-def mira_laplace_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_laplace_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
 
     if "location" in parameters.keys():
         loc = parameters["location"]
@@ -166,47 +158,37 @@ def mira_laplace_to_pyro(
 
 
 def mira_paretotypeI_to_pyro(
-    parameters: Dict[str, float]
+    parameters: ParameterDict,
 ) -> pyro.distributions.Distribution:
-    raise NotImplementedError(
-        "Conversion from MIRA ParetoTypeI distribution to Pyro distribution is not implemented."
-    )
-    # TODO: Confirm that parameters are mapped correctly
     scale = parameters["scale"]
     alpha = parameters["shape"]
     return pyro.distributions.Pareto(scale=scale, alpha=alpha)
 
 
-def mira_poisson_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_poisson_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     rate = parameters["rate"]
     return pyro.distributions.Poisson(rate=rate)
 
 
-def mira_studentt_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_studentt_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
     if "mean" in parameters.keys():
         loc = parameters["mean"]
     elif "location" in parameters.keys():
         loc = parameters["location"]
     else:
-        loc = 0.0
+        loc = torch.tensor(0.0)
 
     if "scale" in parameters.keys():
         scale = parameters["scale"]
     else:
-        scale = 1.0
+        scale = torch.tensor(1.0)
 
     df = parameters["degreesOfFreedom"]
 
     return pyro.distributions.StudentT(df=df, loc=loc, scale=scale)
 
 
-def mira_weibull_to_pyro(
-    parameters: Dict[str, float]
-) -> pyro.distributions.Distribution:
+def mira_weibull_to_pyro(parameters: ParameterDict) -> pyro.distributions.Distribution:
 
     if "scale" in parameters.keys():
         scale = parameters["scale"]
@@ -219,7 +201,7 @@ def mira_weibull_to_pyro(
 
 
 # Key - MIRA distribution type : str
-# Value - MIRA -> Pyro function : Callable[[Dict[str, float]], pyro.distributions.Distribution]
+# Value - MIRA -> Pyro function : Callable[[ParameterDict], pyro.distributions.Distribution]
 # See https://github.com/indralab/mira/blob/main/mira/dkg/resources/probonto.json for MIRA distribution types
 _MIRA_TO_PYRO = {
     "Uniform1": mira_uniform_to_pyro,
@@ -256,6 +238,15 @@ def mira_weibull_to_pyro(
     "Weibull2": mira_weibull_to_pyro,
 }
 
+_TESTED_DISTRIBUTIONS = [
+    "Uniform1",
+    "StandardUniform1",
+    "StandardNormal1",
+    "Normal1",
+    "Normal2",
+    "Normal3",
+]
+
 
 def mira_distribution_to_pyro(
     mira_dist: mira.metamodel.template_model.Distribution,
@@ -265,4 +256,11 @@ def mira_distribution_to_pyro(
             f"Conversion from MIRA distribution type {mira_dist.type} to Pyro distribution is not implemented."
         )
 
-    return _MIRA_TO_PYRO[mira_dist.type](mira_dist.parameters)
+    if mira_dist.type not in _TESTED_DISTRIBUTIONS:
+        warnings.warn(
+            f"Conversion from MIRA distribution type {mira_dist.type} to Pyro distribution has not been tested."
+        )
+
+    parameters = {param.name: torch.as_tensor(param.value) for param in mira_dist}
+
+    return _MIRA_TO_PYRO[mira_dist.type](parameters)