Description Usage Arguments Details References See Also

Draw MCMC samples from a model posterior using the No-U-Turn (NUTS) sampler with dual averaging.

1 2 | ```
sample_tmb_nuts(iter, fn, gr, init, warmup = floor(iter/2), chain = 1,
thin = 1, seed = NULL, control = NULL)
``` |

`iter` |
The number of samples to draw. |

`fn` |
A function that returns the log of the posterior density. |

`gr` |
A function that returns a vector of gradients of the log of
the posterior density (same as |

`init` |
A list of lists containing the initial parameter vectors,
one for each chain or a function. It is strongly recommended to
initialize multiple chains from dispersed points. A of NULL signifies
to use the starting values present in the model (i.e., |

`warmup` |
The number of warmup iterations. |

`chain` |
The chain number, for printing only. |

`thin` |
The thinning rate to apply to samples. Typically not used with NUTS. |

`seed` |
The random seed to use. |

`control` |
A list to control the sampler. See details for further use. |

This function implements algorithm 6 of Hoffman and Gelman
(2014), which includes adaptive step sizes (`eps`

) via an
algorithm called dual averaging. It also includes an adaptation scheme
to tune a diagonal mass matrix (metric) during warmup.

These `fn`

and `gr`

functions must have Jacobians already
applied if there are transformations used.

Hoffman and Gelman (2014). The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15:1593-1623.

colemonnahan/rnuts documentation built on Feb. 13, 2018, 4 p.m.

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