flat-mcmc: Painless general-purpose sampling.
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 qualified Data.Vector.Unboxed as U (Vector, toList, fromList) import qualified Data.Vector as V (fromList) rosenbrock :: Particle -> Double rosenbrock xs = negate (5 * (x1 - x0 ^ 2) ^ 2 + 0.05 * (1 - x0) ^ 2) where [x0, x1] = U.toList xs ensemble :: Ensemble ensemble = V.fromList [ U.fromList [negate 1.0, negate 1.0] , U.fromList [negate 1.0, 1.0] , U.fromList [1.0, negate 1.0] , U.fromList [1.0, 1.0] ] main :: IO () main = withSystemRandom . asGenIO $ mcmc 12500 ensemble rosenbrock
|Versions [faq]||0.1.0.0, 1.0.0, 1.0.1, 1.1.1, 1.2.1, 1.2.2, 1.3.0, 1.4.0, 1.4.1, 1.4.2, 1.5.0, 1.5.1, 1.5.2|
|Dependencies||base (>4 && <6), formatting (==6.*), mcmc-types (>=1.0.1 && <2), monad-par (>=0.3.4.7 && <1), monad-par-extras (>=0.3.3 && <1), mwc-probability (>=1.0.1 && <2), pipes (>4 && <5), primitive, text, transformers, vector (>=0.10 && <1) [details]|
|Source repo||head: git clone http://github.com/jtobin/flat-mcmc.git|
|Uploaded||by JaredTobin at 2016-11-07T01:44:16Z|
|Distributions||LTSHaskell:1.5.0, NixOS:1.5.2, Stackage:1.5.0|
|Downloads||6950 total (19 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
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Last success reported on 2016-11-17 [all 1 reports]
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