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

Source: R/sample_tmb_deprecated.R
sample_tmb_hmc.Rd

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

Usage

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

Arguments

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

Can be either a list containing a vector for each chain, a function which returns a vector of parameters, or NULL which specifies to use the MLE as stored in the admodel.hes file. It is generally recommended to use dispersed initial values to improve diagnostic checks (starting from the same point makes it less likely to find multiple modes).

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.

Value

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

Details

This function implements algorithm 5 of Hoffman and Gelman (2014), which includes adaptive step sizes (eps) via an algorithm called dual averaging.

References

  • 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.

See also

sample_tmb

sample_tmb