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
Downloads
- mighty-metropolis-1.2.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
Candidates
- No Candidates
| 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 |
|---|---|
| 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] |
| License | MIT |
| Author | Jared Tobin |
| Maintainer | jared@jtobin.ca |
| Uploaded | by JaredTobin at 2016-12-21T21:14:54Z |
| Category | Numeric |
| Home page | http://github.com/jtobin/mighty-metropolis |
| Source repo | head: git clone http://github.com/jtobin/mighty-metropolis.git |
| Reverse Dependencies | 1 direct, 1 indirect [details] |
| Downloads | 5674 total (25 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2016-12-21 [all 1 reports] |