Description Usage Arguments Details Value References See Also

Draw MCMC samples from a model posterior using a static HMC sampler.

1 2 | ```
sample_tmb_hmc(iter, fn, gr, init, L, eps, warmup = floor(iter/2),
seed = NULL, chain = 1, thin = 1, 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., |

`L` |
The number of leapfrog steps to take. The NUTS algorithm does
not require this as an input. If |

`eps` |
The step size. If a numeric value is passed, it will be used
throughout the entire chain. A |

`warmup` |
The number of warmup iterations. |

`seed` |
The random seed to use. |

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

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

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

This function implements algorithm 5 of Hoffman and Gelman
(2014), which includes adaptive step sizes (`eps`

) via an
algorithm called dual averaging.

A list containing samples ('par') and algorithm details such as step size adaptation and acceptance probabilities per iteration ('sampler_params').

Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo.

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

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

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