diff --git a/CHANGELOG b/CHANGELOG
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -1,5 +1,8 @@
 	# Changelog
 
+	- 2.0.3 (2018-05-09)
+	* Add inverse Gaussian (Wald) distribution
+	
 	- 2.0.2 (2018-01-30)
 	* Add negative binomial distribution
 
diff --git a/README.md b/README.md
--- a/README.md
+++ b/README.md
@@ -13,28 +13,62 @@
 state-passing automatically by using a `PrimMonad` like `IO` or `ST s` under
 the hood.
 
+
 Examples
 --------
 
-Transform a distribution's support while leaving its density structure
+* Transform a distribution's support while leaving its density structure
 invariant:
 
-    -- uniform over [0, 1] to uniform over [1, 2]
-    succ <$> uniform
+      -- uniform over [0, 1] transformed to uniform over [1, 2]
+      succ <$> uniform
 
-Sequence distributions together using bind:
+* Sequence distributions together using bind:
 
-    -- a beta-binomial composite distribution
-    beta 1 10 >>= binomial 10
+      -- a beta-binomial composite distribution
+      beta 1 10 >>= binomial 10
 
-Use do-notation to build complex joint distributions from composable,
+* Use do-notation to build complex joint distributions from composable,
 local conditionals:
 
-    hierarchicalModel = do
-      [c, d, e, f] <- replicateM 4 $ uniformR (1, 10)
-      a <- gamma c d
-      b <- gamma e f
-      p <- beta a b
-      n <- uniformR (5, 10)
-      binomial n p
+      hierarchicalModel = do
+        [c, d, e, f] <- replicateM 4 $ uniformR (1, 10)
+        a <- gamma c d
+        b <- gamma e f
+        p <- beta a b
+        n <- uniformR (5, 10)
+        binomial n p
 
+
+
+Included probability distributions
+-------------
+
+* Continuous
+
+  * Uniform
+  * Normal
+  * Log-Normal
+  * Exponential
+  * Inverse Gaussian
+  * Laplace
+  * Gamma
+  * Inverse Gamma
+  * Weibull
+  * Chi-squared
+  * Beta
+  * Student t
+  * Pareto
+  * Dirichlet process
+  * Symmetric Dirichlet process  
+
+* Discrete
+
+  * Discrete uniform
+  * Zipf-Mandelbrot
+  * Categorical
+  * Bernoulli
+  * Binomial
+  * Negative Binomial
+  * Multinomial
+  * Poisson
diff --git a/mwc-probability.cabal b/mwc-probability.cabal
--- a/mwc-probability.cabal
+++ b/mwc-probability.cabal
@@ -1,5 +1,5 @@
 name:                mwc-probability
-version:             2.0.2
+version:             2.0.3
 homepage:            http://github.com/jtobin/mwc-probability
 license:             MIT
 license-file:        LICENSE
@@ -8,7 +8,7 @@
 category:            Math
 build-type:          Simple
 cabal-version:       >= 1.10
-tested-with:         GHC == 8.0.2, GHC == 8.2.2                     
+tested-with:         GHC == 8.0.2, GHC == 8.2.2 , GHC == 8.4.2
 synopsis:            Sampling function-based probability distributions.
 description:
 
diff --git a/src/System/Random/MWC/Probability.hs b/src/System/Random/MWC/Probability.hs
--- a/src/System/Random/MWC/Probability.hs
+++ b/src/System/Random/MWC/Probability.hs
@@ -73,6 +73,7 @@
   , isoNormal    
   , logNormal
   , exponential
+  , inverseGaussian
   , laplace
   , gamma
   , inverseGamma
@@ -333,6 +334,23 @@
   :: (Traversable f, PrimMonad m) => f Double -> Double -> Prob m (f Double)
 isoNormal ms sd = traverse (`normal` sd) ms
 {-# INLINABLE isoNormal #-}
+
+-- | The inverse Gaussian (also known as Wald) distribution.
+--
+-- Both the mean parameter 'mu' and the shape parameter 'lambda' must be positive.
+inverseGaussian :: PrimMonad m => Double -> Double -> Prob m Double
+inverseGaussian lambda mu = do
+  nu <- standardNormal
+  let y = nu ** 2
+      s =  sqrt (4 * mu * lambda * y + mu ** 2  * y ** 2)
+      x = mu * (1 + 1 / (2 * lambda) * (mu * y - s))
+      thresh = mu / (mu + x)
+  z <- uniform
+  if z <= thresh
+    then return x
+    else return (mu ** 2 / x)
+{-# INLINABLE inverseGaussian #-}    
+  
 
 -- | The Poisson distribution.
 poisson :: PrimMonad m => Double -> Prob m Int