hasty-hamiltonian: Speedy traversal through parameter space.
Gradient-based traversal through parameter space.
This implementation of HMC algorithm uses lens as a means to operate over
generic indexed traversable functors, so you can expect it to work if your
target function takes a list, vector, map, sequence, etc. as its argument.
If you don't want to calculate your gradients by hand you can use the handy ad library for automatic differentiation.
Exports a mcmc function that prints a trace to stdout, as well as a
hamiltonian transition operator that can be used more generally.
import Numeric.AD (grad) import Numeric.MCMC.Hamiltonian target :: RealFloat a => [a] -> a target [x0, x1] = negate ((x0 + 2 * x1 - 7) ^ 2 + (2 * x0 + x1 - 5) ^ 2) gTarget :: [Double] -> [Double] gTarget = grad target booth :: Target [Double] booth = Target target (Just gTarget) main :: IO () main = withSystemRandom . asGenIO $ mcmc 10000 0.05 20 [0, 0] booth
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- hasty-hamiltonian-1.1.0.tar.gz [browse] (Cabal source package)
 - Package description (as included in the package)
 
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| Versions [RSS] | 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.3.0, 1.3.2, 1.3.3, 1.3.4 | 
|---|---|
| Dependencies | base (<5), ghc-prim, lens, mcmc-types (>=1.0.1), mwc-probability (>=1.0.1), pipes, primitive, transformers [details] | 
| License | MIT | 
| Author | Jared Tobin | 
| Maintainer | jared@jtobin.ca | 
| Category | Numeric | 
| Home page | http://jtobin.github.com/hasty-hamiltonian | 
| Source repo | head: git clone http://github.com/jtobin/hasty-hamiltonian.git | 
| Uploaded | by JaredTobin at 2015-10-08T08:22:52Z | 
| Distributions | LTSHaskell:1.3.4, NixOS:1.3.4, Stackage:1.3.4 | 
| Reverse Dependencies | 1 direct, 1 indirect [details] | 
| Downloads | 7063 total (16 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 2015-10-12 [all 1 reports]  |