# The hasty-hamiltonian package

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, a `chain`

function
for collecting results in memory, and 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

## Properties

Versions | 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.3.0 |
---|---|

Dependencies | base (>=4 && <6), kan-extensions (==5.*), lens (==4.*), mcmc-types (>=1.0.1), mwc-probability (>=1.0.1), pipes (==4.*), primitive (>=0.5 && <1.0), transformers (>=0.5 && <1.0) [details] |

License | MIT |

Author | Jared Tobin |

Maintainer | jared@jtobin.ca |

Category | Numeric |

Home page | http://github.com/jtobin/hasty-hamiltonian |

Source repository | head: git clone http://github.com/jtobin/hasty-hamiltonian.git |

Uploaded | Wed Dec 21 21:15:12 UTC 2016 by JaredTobin |

Distributions | LTSHaskell:1.3.0, NixOS:1.3.0, Stackage:1.3.0, Tumbleweed:1.3.0 |

Downloads | 565 total (16 in the last 30 days) |

Rating | 0.0 (0 ratings) [clear rating] |

Status | Docs available [build log] Last success reported on 2016-12-21 [all 1 reports] Hackage Matrix CI |

## Downloads

- hasty-hamiltonian-1.3.0.tar.gz [browse] (Cabal source package)
- Package description (included in the package)