diff --git a/README.md b/README.md
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 ## ProbFX
 
 #### Prelude
-ProbFX is a library for probabilistic programming using algebraic effects that implements the paper [**Modular Probabilistic Models via Algebraic Effects**](https://github.com/min-nguyen/prob-fx/blob/master/paper.pdf) -- this paper provides a comprehensive motivation and walkthrough of this library. To have a more interative and visual play-around with ProbFX, please see https://github.com/min-nguyen/prob-fx: this corresponds parts of the paper to the implementation, and also provides an executable version of ProbFX as a script!
+ProbFX is a library for probabilistic programming using algebraic effects that implements the paper [**Modular Probabilistic Models via Algebraic Effects**](https://github.com/min-nguyen/prob-fx/blob/master/paper.pdf) -- this paper provides a comprehensive motivation and walkthrough of this library. To have a more interactive and visual play-around with ProbFX, please see https://github.com/min-nguyen/prob-fx: this corresponds parts of the paper to the implementation, and also provides an executable version of ProbFX as a script!
 
 #### Description
 ProbFx is a PPL that places emphasis on being able to define modular and reusable probabilistic models, where the decision to `sample` or `observe` against a random variable or distribution of a model is delayed until the point of execution; this allows a model to be defined just *once* and then reused for a variety of applications. We also implement a compositional approach towards model execution (inference) by using effect handlers. 
diff --git a/prob-fx.cabal b/prob-fx.cabal
--- a/prob-fx.cabal
+++ b/prob-fx.cabal
@@ -1,6 +1,6 @@
 cabal-version:       3.0
 name:                prob-fx
-version:             0.1.0.0
+version:             0.1.0.1
 license:             BSD-3-Clause
 license-file:        LICENSE.md
 copyright:           2022 Minh Nguyen
diff --git a/src/Model.hs b/src/Model.hs
--- a/src/Model.hs
+++ b/src/Model.hs
@@ -183,7 +183,7 @@
 normal mu sigma field = Model $ do
   let tag = Just $ varToStr field
   maybe_y <- ask @env field
-  call (Dist (Normal mu sigma) maybe_y tag)
+  call (Dist (NormalDist mu sigma) maybe_y tag)
 
 normal' :: 
   -- | Mean
@@ -192,7 +192,7 @@
   -> Double 
   -> Model env es Double
 normal' mu sigma = Model $ do
-  call (Dist (Normal mu sigma) Nothing Nothing)
+  call (Dist (NormalDist mu sigma) Nothing Nothing)
 
 halfNormal :: forall env es x. Observable env x Double => 
      Double 
diff --git a/src/PrimDist.hs b/src/PrimDist.hs
--- a/src/PrimDist.hs
+++ b/src/PrimDist.hs
@@ -99,7 +99,7 @@
     :: Double           -- ^ Shape k
     -> Double           -- ^ Scale θ
     -> PrimDist Double
-  Normal        
+  NormalDist      
     :: Double           -- ^ Mean
     -> Double           -- ^ Standard deviation
     -> PrimDist Double
@@ -115,7 +115,7 @@
     -> PrimDist Double  
 
 instance Eq (PrimDist a) where
-  (==) (Normal m s) (Normal m' s') = m == m' && s == s'
+  (==) (NormalDist m s) (NormalDist m' s') = m == m' && s == s'
   (==) (CauchyDist m s) (CauchyDist m' s') = m == m' && s == s'
   (==) (HalfCauchyDist s) (HalfCauchyDist s') = s == s'
   (==) (HalfNormalDist s) (HalfNormalDist s') = s == s'
@@ -137,8 +137,8 @@
    "CauchyDist(" ++ show mu ++ ", " ++ show sigma ++ ", " ++ ")"
   show (HalfCauchyDist sigma) =
    "HalfCauchyDist(" ++ show sigma ++ ", " ++ ")"
-  show (Normal mu sigma) =
-   "Normal(" ++ show mu ++ ", " ++ show sigma ++ ", " ++ ")"
+  show (NormalDist mu sigma) =
+   "NormalDist(" ++ show mu ++ ", " ++ show sigma ++ ", " ++ ")"
   show (HalfNormalDist sigma) =
    "HalfNormalDist(" ++ show sigma ++ ", " ++ ")"
   show (BernoulliDist p) =
@@ -180,7 +180,7 @@
 primDistPrf d = case d of
   HalfCauchyDist {} -> IsPrimVal
   CauchyDist {} -> IsPrimVal
-  Normal {} -> IsPrimVal
+  NormalDist {} -> IsPrimVal
   HalfNormalDist  {} -> IsPrimVal
   UniformDist  {} -> IsPrimVal
   DiscrUniformDist {} -> IsPrimVal
@@ -211,7 +211,7 @@
   createSampler (sampleCauchy μ σ)
 sample (HalfNormalDist σ )  =
   createSampler (sampleNormal 0 σ) >>= pure . abs
-sample (Normal μ σ )  =
+sample (NormalDist μ σ )  =
   createSampler (sampleNormal μ σ)
 sample (UniformDist min max )  =
   createSampler (sampleUniform min max)
@@ -258,7 +258,7 @@
 prob (HalfNormalDist σ) y
   = if y < 0 then 0 else
             2 * density (normalDistr 0 σ) y
-prob (Normal μ σ) y
+prob (NormalDist μ σ) y
   = density (normalDistr μ σ) y
 prob (UniformDist min max) y
   = density (uniformDistr min max) y