Painless general-purpose sampling.
|Version on this page:||1.0.1|
|LTS Haskell 20.23:||1.5.2|
|Stackage Nightly 2022-11-17:||1.5.2|
|Latest on Hackage:||1.5.2|
Maintained by email@example.com
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Module documentation for 1.0.1
Depends on 9 packages(full list with versions):
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
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