mighty-metropolis: The Metropolis algorithm.
The classic Metropolis algorithm.
Wander around parameter space according to a simple spherical Gaussian distribution.
Exports a mcmc
function that prints a trace to stdout, a chain
function
for collecting results in-memory, and a metropolis
transition operator that
can be used more generally.
import Numeric.MCMC.Metropolis rosenbrock :: [Double] -> Double rosenbrock [x0, x1] = negate (5 *(x1 - x0 ^ 2) ^ 2 + 0.05 * (1 - x0) ^ 2) main :: IO () main = withSystemRandom . asGenIO $ mcmc 10000 1 [0, 0] rosenbrock
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- mighty-metropolis-2.0.0.tar.gz [browse] (Cabal source package)
- Package description (revised from the package)
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Versions [RSS] | 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.1.0, 1.2.0, 2.0.0 |
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Dependencies | base (>=4 && <6), kan-extensions (>=5 && <6), mcmc-types (>=1.0.1), mwc-probability (>=1.0.1), pipes (>=4 && <5), primitive (>=0.6 && <1.0), transformers (>=0.5 && <1.0) [details] |
Tested with | ghc ==8.2.2, ghc ==8.8.3 |
License | MIT |
Author | Jared Tobin |
Maintainer | jared@jtobin.ca |
Revised | Revision 1 made by JaredTobin at 2024-11-09T07:40:05Z |
Category | Numeric |
Home page | http://github.com/jtobin/mighty-metropolis |
Source repo | head: git clone http://github.com/jtobin/mighty-metropolis.git |
Uploaded | by JaredTobin at 2020-05-21T17:22:32Z |
Distributions | LTSHaskell:2.0.0, NixOS:2.0.0, Stackage:2.0.0 |
Reverse Dependencies | 1 direct, 1 indirect [details] |
Downloads | 5440 total (28 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 2020-05-21 [all 1 reports] |