diff --git a/LICENSE b/LICENSE
new file mode 100644
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,339 @@
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diff --git a/NestedSampling.cabal b/NestedSampling.cabal
new file mode 100644
--- /dev/null
+++ b/NestedSampling.cabal
@@ -0,0 +1,43 @@
+Name:                NestedSampling
+Version:             0.1.1
+Synopsis:            A port of John Skilling's nested sampling C code to Haskell.
+
+Description:         Nested Sampling is a numerical algorithm for approximate Bayesian
+                     inference. It generates samples from the posterior
+                     distribution but its main purpose is to estimate the
+                     evidence P(M|D) of the model conditioned on the observed
+                     data. More information on Nested Sampling is available
+                     at http://en.wikipedia.org/wiki/Nested_sampling_algorithm.
+
+                     The original code can be found at
+                     http://www.inference.phy.cam.ac.uk/bayesys/sivia/
+                     along with documentation at
+                     http://www.inference.phy.cam.ac.uk/bayesys/.
+                     An example program called lighthouse.hs is included.
+
+                     So far, only the simple demonstration file called mininest.c
+                     has been ported. There is a more sophisticated C library
+                     available at
+                     http://www.inference.phy.cam.ac.uk/bayesys/nest/nest.tar.gz
+                     but it has not been ported to Haskell yet.
+
+Homepage:            https://github.com/ijt/haskell_nested_sampling
+License:             GPL-2
+License-file:        LICENSE
+Author:              Issac Trotts
+Maintainer:          issac.trotts@gmail.com
+Copyright:           (C) Sivia, Skilling 2006, Trotts 2011
+Category:            Statistics
+Build-type:          Simple
+Extra-source-files:  lighthouse.hs README
+Cabal-version:       >=1.8
+
+Library
+  Exposed-modules:     Statistics.MiniNest
+  Build-depends:       base >= 4 && < 5, random
+  hs-source-dirs:      lib
+
+Executable lighthouse
+  Main-Is: lighthouse.hs
+  Build-depends: base >= 4 && < 5, random, vector, NestedSampling
+
diff --git a/README b/README
new file mode 100644
--- /dev/null
+++ b/README
@@ -0,0 +1,25 @@
+The code here is a fairly straightforward translation of the tutorial
+nested sampling code from Skilling and Sivia. The translation was
+done by Issac Trotts starting in June 2011.
+
+What follows is an adaptation of the original README:
+
+This directory holds little toy nested-sampling programs
+in Haskell, adapted from the C code in the update of Devinder's book "Data
+Analysis: a Bayesian Tutorial" (2nd edition) OUP 2006.
+
+To get started, install Haskell (GHC), then run
+$ cabal install
+
+Try out the example program like this:
+
+$ lighthouse 
+logZ: -160.48 +- 0.17
+information: 2.90 nats
+1000 samples
+
+x = 1.25 +- 0.18
+y = 1.00 +- 0.20
+
+Details can be found at the top of lighthouse.hs.
+
diff --git a/Setup.hs b/Setup.hs
new file mode 100644
--- /dev/null
+++ b/Setup.hs
@@ -0,0 +1,2 @@
+import Distribution.Simple
+main = defaultMain
diff --git a/lib/Statistics/MiniNest.hs b/lib/Statistics/MiniNest.hs
new file mode 100644
--- /dev/null
+++ b/lib/Statistics/MiniNest.hs
@@ -0,0 +1,115 @@
+module Statistics.MiniNest where
+
+import Control.Monad (forM)
+import Data.IORef
+import Data.List (sort)
+import System.Random (randomRIO)
+import Text.Printf
+
+-- logarithmic addition log(exp(x)+exp(y))
+plus :: Double -> Double -> Double
+plus x y
+     | x > y = x + log (1 + exp (y-x))
+     | otherwise = y + log (1 + exp (x-y))
+
+data NestedSamplingResult a = NestedSamplingResult {
+    nsLogZ :: Double,
+    nsLogZdelta :: Double,  -- evidence +- deviation
+    nsInfoNats :: Double,   -- information in nats
+    nsSamples :: [a] }
+
+instance Show (NestedSamplingResult a) where
+    show result =
+        (printf "logZ: %.2f +- %.2f\n" (nsLogZ result) (nsLogZdelta result) ++ 
+         printf "information: %.2f nats\n" (nsInfoNats result) ++
+         printf "%i samples\n" (length $ nsSamples result))
+
+class SamplingObject a where
+   setLogWt :: a -> Double -> a
+   getLogWt :: a -> Double
+   getLogL :: a -> Double
+
+-- |nestedSampling computes the evidence Z and samples from the posterior.
+-- Args:
+--   priorSamples: a list of samples from the prior.
+--   explore: a function that evolves an object within a likelihood constraint.
+--   iterations: number of iterations to run.
+nestedSampling :: (Ord a, SamplingObject a) => [a] -> (a -> Double -> IO a) -> Int -> IO (NestedSamplingResult a)
+nestedSampling priorSamples explore iterations = do
+    let n = length priorSamples
+
+    -- Collection of n objects
+    objsRef <- newIORef priorSamples
+    samplesRef <- newIORef []              -- Posterior samples
+    hRef <- newIORef 0                     -- Information, initially 0
+    logZRef <- newIORef (-10**37)          -- ln(Evidence Z, initially 0)
+
+    -- Outermost interval of prior mass
+    -- ln(width in prior mass)
+    logWidthRef <- newIORef $ getLogWidth n
+
+    -- NESTED SAMPLING LOOP ______________________________________________
+    forM [1..iterations] (\nest -> do
+        -- Worst object in collection, with Weight = width * Likelihood
+        objs <- readIORef objsRef
+        let worst = head $ sort objs
+        logwidth <- readIORef logWidthRef
+        let worst' = setLogWt worst (logwidth + (getLogL worst))
+
+        -- Update Evidence Z and Information H
+        logZ <- readIORef logZRef
+        h <- readIORef hRef
+        let logZnew = plus logZ (getLogWt worst')
+        writeIORef hRef $ (exp $ getLogWt worst' - logZnew) * (getLogL worst')
+            + (exp $ logZ - logZnew) * (h + logZ) - logZnew
+        writeIORef logZRef logZnew
+
+        -- Posterior Samples (optional)
+        oldSamples <- readIORef samplesRef
+        writeIORef samplesRef (worst' : oldSamples)
+
+        -- Kill worst object.
+        let objs' = drop 1 $ sort objs
+        writeIORef objsRef objs'
+
+        -- Copy another object at random.
+        objToCopy <- choice objs'
+
+        -- new likelihood constraint
+        let logLstar = getLogL worst'
+
+        -- Evolve copied object within constraint
+        mutatedCopy <- explore objToCopy logLstar
+
+        -- Save copied and mutated object.
+        writeIORef objsRef (mutatedCopy : objs')
+
+        -- Shrink interval
+        writeIORef logWidthRef (logwidth - 1.0 / fromIntegral n))
+
+    -- Exit with evidence Z, information H, and optional posterior Samples
+    logZ <- readIORef logZRef
+    h <- readIORef hRef
+    samples <- readIORef samplesRef
+    return $ NestedSamplingResult {
+        nsLogZ=logZ,
+        nsLogZdelta=sqrt (h / fromIntegral n),  -- evidence +- deviation
+        nsInfoNats=h,                           -- information in nats
+        nsSamples=samples
+    }
+
+-- |choice chooses uniformly at random from a list.
+choice :: [a] -> IO a
+choice [] = error "No items specified for choice."
+choice [x] = return x
+choice xs = do
+    let n = length xs
+    k <- randomRIO (0, n-1)
+    return $ xs !! k
+
+floatRatio :: Int -> Int -> Float
+floatRatio n1 n2 = fromIntegral n1 / fromIntegral n2
+
+getLogWidth :: Int -> Double
+getLogWidth n = log $ 1.0 - exp(-1.0 / fromIntegral n)
+
diff --git a/lighthouse.hs b/lighthouse.hs
new file mode 100644
--- /dev/null
+++ b/lighthouse.hs
@@ -0,0 +1,157 @@
+-- lighthouse.hs     "LIGHTHOUSE" NESTED SAMPLING APPLICATION
+-- (GNU General Public License software, (C) Sivia and Skilling 2006)
+--              u=0                                 u=1
+--               -------------------------------------
+--          y=2 |:::::::::::::::::::::::::::::::::::::| v=1
+--              |::::::::::::::::::::::LIGHT::::::::::|
+--         north|::::::::::::::::::::::HOUSE::::::::::|
+--              |:::::::::::::::::::::::::::::::::::::|
+--              |:::::::::::::::::::::::::::::::::::::|
+--          y=0 |:::::::::::::::::::::::::::::::::::::| v=0
+-- --*--------------*----*--------*-**--**--*-*-------------*--------
+--             x=-2          coastline -->east      x=2
+-- Problem:
+--  Lighthouse at (x,y) emitted n flashes observed at D[.] on coast.
+-- Inputs:
+--  Prior(u)    is uniform (=1) over (0,1), mapped to x = 4*u - 2; and
+--  Prior(v)    is uniform (=1) over (0,1), mapped to y = 2*v; so that
+--  Position    is 2-dimensional -2 < x < 2, 0 < y < 2 with flat prior
+--  Likelihood  is L(x,y) = PRODUCT[k] (y/pi) / ((D[k] - x)^2 + y^2)
+-- Outputs:
+--  Evidence    is Z = INTEGRAL L(x,y) Prior(x,y) dxdy
+--  Posterior   is P(x,y) = L(x,y) / Z estimating lighthouse position
+--  Information is H = INTEGRAL P(x,y) log(P(x,y)/Prior(x,y)) dxdy
+
+import qualified Data.Vector.Unboxed as UV
+import Control.Monad (mapM)
+import Statistics.MiniNest
+import System.Random (randomIO)
+import Text.Printf
+
+data Lighthouse = Lighthouse {
+    lhU :: Double,
+    lhV :: Double,
+    lhX :: Double,
+    lhY :: Double,
+    lhLogL :: Double,
+    lhLogWt :: Double 
+} deriving (Eq, Show)
+
+instance Ord Lighthouse where
+    a <= b = lhLogL a <= lhLogL b
+
+instance SamplingObject Lighthouse where
+   setLogWt lh newLogWt = lh { lhLogWt = newLogWt }
+   getLogWt lh = lhLogWt lh
+   getLogL lh = lhLogL lh
+
+logLhoodOfData :: UV.Vector Double -> Double -> Double -> Double
+logLhoodOfData observations x y = UV.sum $ UV.map term observations
+    where term dk = log (y / pi) - log ((dk - x)*(dk - x) + y*y)
+
+-- logLikelihood function
+-- x: Easterly position
+-- y: Northerly position
+logLhood :: Double -> Double -> Double
+logLhood x y = logLhoodOfData lhData x y
+
+lhData = UV.fromList [4.73,  0.45, -1.73,  1.09,  2.19,  0.12, 1.31,
+                      1.00,  1.32,  1.07,  0.86, -0.49, -2.59,  1.73,  2.11,
+                      1.61,  4.98,  1.71, 2.23,-57.20,  0.96,  1.25, -1.56,
+                      2.45, 1.19,  2.17,-10.66,  1.91, -4.16, 1.92,  0.10,  1.98,
+                      -2.51, 5.55, -0.47,  1.91,  0.95, -0.78, -0.84,  1.72,
+                      -0.01,  1.48, 2.70,  1.21,  4.41, -4.79,  1.33,  0.81,
+                      0.20,  1.58,  1.29, 16.19,  2.75, -2.38, -1.79,
+                      6.50,-18.53,  0.72,  0.94,  3.64, 1.94, -0.11, 1.57,  0.57]
+
+-- |Sample from U[0,1]
+uniform :: IO Double
+uniform = randomIO
+
+sampleFromPrior :: IO Lighthouse
+sampleFromPrior = do
+    u <- uniform
+    v <- uniform
+    let x=4*u - 2
+        y=2*v
+    return $ Lighthouse u v x y (logLhood x y) 0
+
+-- |Evolve Lighthouse within likelihood constraint
+-- obj: Lighthouse being evolved
+-- logLstar: Likelihood constraint L > Lstar
+explore :: Lighthouse -> Double -> IO Lighthouse
+explore obj logLstar =
+    explore' step m accept reject (lhU obj) (lhV obj) (lhX obj) (lhY obj)
+        (lhLogL obj)
+    where step = 0.1      -- Initial guess suitable step-size in (0,1)
+          m = 20          -- MCMC counter (pre-judged # steps)
+          accept = 0      -- # MCMC acceptances
+          reject = 0      -- # MCMC rejections
+          explore' step m accept reject u v x y logL = do
+            -- Trial Lighthouse
+            unif1 <- uniform
+            unif2 <- uniform
+            let u' = wrapAround $ u + step * (2*unif1 - 1)  -- |move| < step
+                v' = wrapAround $ v + step * (2*unif2 - 1)  -- |move| < step
+                x' = 4*u' - 2    -- map to x
+                y' = 2*v'        -- map to y
+                logL' = logLhood x' y'
+
+            -- Accept if and only if within hard likelihood constraint
+            obj' <- 
+                if logL' > logLstar
+                    then return $ Lighthouse u' v' x' y' logL' (lhLogWt obj)
+                    else return $ Lighthouse u v x y logL (lhLogWt obj)
+            (accept, reject) <- if logL' > logLstar
+                                    then return (accept + 1, reject)
+                                    else return (accept, reject + 1)
+            
+            -- Refine step-size to let acceptance ratio converge around 50%
+            step <- if accept > reject
+                        then return $ step * exp(1.0 / accept)
+                        else return step
+            step <- if accept < reject
+                        then return $ step / exp(1.0 / reject)
+                        else return step
+            if m == 0
+                then return obj'
+                else explore' step (m-1) accept reject (lhU obj') (lhV obj')
+                        (lhX obj') (lhY obj') (lhLogL obj')
+
+wrapAround :: Double -> Double
+wrapAround x = x - (fromIntegral $ floor x)
+
+data Stats = Stats { meanX :: Double,
+                     meanY :: Double,
+                     stddevX :: Double,
+                     stddevY :: Double }
+
+instance Show Stats where
+    show s = (printf "x = %.2f +- %.2f\n" (meanX s) (stddevX s) ++
+              printf "y = %.2f +- %.2f\n" (meanY s) (stddevY s))
+
+-- Posterior properties, here mean and stddev of x,y
+-- Args:
+--  samples: Objects defining posterior
+--  logZ: Evidence (= total weight = SUM[Samples] Weight)
+getStats :: [Lighthouse] -> Double -> Stats
+getStats samples logZ =
+    Stats {meanX=x,
+           meanY=y,
+           stddevX=sqrt $ xx - x*x,
+           stddevY=sqrt $ yy - y*y }
+    where weightsSamples = [(exp (lhLogWt s - logZ), s) | s <- samples]
+          x = sum [w*(lhX s) | (w,s) <- weightsSamples]
+          y = sum [w*(lhY s) | (w,s) <- weightsSamples]
+          xx = sum [w*(lhX s)^2 | (w,s) <- weightsSamples]
+          yy = sum [w*(lhY s)^2 | (w,s) <- weightsSamples]
+
+main = do
+    let n = 100                -- # number of candidate lighthouses
+    let maxIterations = 1000   -- # iterates
+    priorSamples <- mapM (\_ -> sampleFromPrior) [1..n]         
+    result <- nestedSampling priorSamples explore maxIterations
+    let stats = getStats (nsSamples result) (nsLogZ result) 
+    print result
+    print stats
+
