Painless general-purpose sampling.


Version on this page:1.0.1
LTS Haskell 20.26:1.5.2
Stackage Nightly 2022-11-17:1.5.2
Latest on Hackage:1.5.2

See all snapshots flat-mcmc appears in

MIT licensed by Jared Tobin
Maintained by [email protected]
This version can be pinned in stack with:flat-mcmc-1.0.1@sha256:4730534f2f0732bc80ff18f7d0af456ec21f613ae765b39f6ffe48200b07aa91,2632

Module documentation for 1.0.1

flat-mcmc is a Haskell library for painless, efficient, general-purpose sampling from continuous distributions.

flat-mcmc uses an ensemble sampler that is invariant to affine transformations of space. It wanders a target probability distribution's parameter space as if it had been "flattened" or "unstretched" in some sense, allowing many particles to explore it locally and in parallel.

In general this sampler is useful when you want decent performance without dealing with any tuning parameters or local proposal distributions.

flat-mcmc exports an mcmc function that prints a trace to stdout, as well as a flat transition operator that can be used more generally.

import Numeric.MCMC.Flat
import Data.Vector (Vector, toList, fromList)

rosenbrock :: Vector Double -> Double
rosenbrock xs = negate (5  *(x1 - x0 ^ 2) ^ 2 + 0.05 * (1 - x0) ^ 2) where
  [x0, x1] = toList xs

ensemble :: Ensemble
ensemble = fromList [
    fromList [negate 1.0, negate 1.0]
  , fromList [negate 1.0, 1.0]
  , fromList [1.0, negate 1.0]
  , fromList [1.0, 1.0]

main :: IO ()
main = withSystemRandom . asGenIO $ mcmc 12500 ensemble rosenbrock