# 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, 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

## Downloads

- hasty-hamiltonian-1.3.4.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)

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 |
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Dependencies | base (>=4 && <6), kan-extensions (>=5 && <6), lens (>=4 && <6), mcmc-types (>=1.0.1), mwc-probability (>=2.0 && <3), pipes (>=4 && <5), 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 repo | head: git clone http://github.com/jtobin/hasty-hamiltonian.git |

Uploaded | by JaredTobin at 2021-02-21T07:54:21Z |

Distributions | LTSHaskell:1.3.4, NixOS:1.3.4, Stackage:1.3.4 |

Downloads | 6178 total (20 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 2021-02-21 [all 1 reports] |