R: Draw MCMC samples from a model posterior using a static HMC...

sample_tmb_hmc {adnuts}

R Documentation

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

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

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 `fn`).
`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., `obj$par`) for all chains.
`L`	The number of leapfrog steps to take. The NUTS algorithm does not require this as an input. If `L=1` this function will perform Langevin sampling. In some contexts `L` can roughly be thought of as a thinning rate.
`eps`	The step size. If a numeric value is passed, it will be used throughout the entire chain. A `NULL` value will initiate sampler_params of `eps` using the dual averaging algorithm during the first `warmup` steps.
`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.