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

Source: R/sample_tmb_deprecated.R

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

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

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.
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.

Details

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.

References

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