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
mcmcfunction that prints a trace to stdout, as well as aflattransition 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
Downloads
- flat-mcmc-1.0.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
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| Versions [RSS] | 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 (<5), mcmc-types (>=1.0.1 && <2), monad-par, monad-par-extras, mwc-probability (>=1.0.1 && <2), pipes (>=4 && <5), primitive, transformers, vector [details] |
| License | MIT |
| Author | Jared Tobin |
| Maintainer | jared@jtobin.ca |
| Uploaded | by JaredTobin at 2016-04-06T13:34:10Z |
| Category | Math |
| Home page | http://jtobin.github.com/flat-mcmc |
| Source repo | head: git clone http://github.com/jtobin/flat-mcmc.git |
| Distributions | |
| Reverse Dependencies | 1 direct, 0 indirect [details] |
| Downloads | 8926 total (60 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
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| Status | Docs available [build log] Last success reported on 2016-04-06 [all 1 reports] |