-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | A port of John Skilling's nested sampling C code to Haskell.
--   
--   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:<i></i>en.wikipedia.org<i>wiki</i>Nested_sampling_algorithm. The
--   original code can be found at
--   http:<i></i>www.inference.phy.cam.ac.uk<i>bayesys</i>sivia/ along with
--   documentation at
--   http:<i></i>www.inference.phy.cam.ac.uk<i>bayesys</i>. 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:<i></i>www.inference.phy.cam.ac.uk<i>bayesys</i>nest/nest.tar.gz
--   but it has not been ported to Haskell yet.
@package NestedSampling
@version 0.1.1

module Statistics.MiniNest
plus :: Double -> Double -> Double
data NestedSamplingResult a
NestedSamplingResult :: Double -> Double -> Double -> [a] -> NestedSamplingResult a
nsLogZ :: NestedSamplingResult a -> Double
nsLogZdelta :: NestedSamplingResult a -> Double
nsInfoNats :: NestedSamplingResult a -> Double
nsSamples :: NestedSamplingResult a -> [a]
class SamplingObject a
setLogWt :: SamplingObject a => a -> Double -> a
getLogWt :: SamplingObject a => a -> Double
getLogL :: SamplingObject a => 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)

-- | choice chooses uniformly at random from a list.
choice :: [a] -> IO a
floatRatio :: Int -> Int -> Float
getLogWidth :: Int -> Double
instance Show (NestedSamplingResult a)