ISandIMH: Monte Carlo sampling using the iterated Laplace...

In iterLap: Approximate Probability Densities by Iterated Laplace Approximations

Monte Carlo sampling using the iterated Laplace approximation.

Description

Use iterated Laplace approximation as a proposal for importance sampling or the independence Metropolis Hastings algorithm.

Usage

IS(obj, nSim, df = 4, post, vectorized = FALSE, cores = 1, ...)

IMH(obj, nSim, df = 4, post, vectorized = FALSE, cores = 1, ...)

Arguments

obj	an object of class "mixDist"
nSim	number of simulations
df	degrees of freedom of the mixture of t distributions proposal
post	log-posterior density
vectorized	Logical determining, whether post is vectorized
cores	number of cores you want to use to evaluate the target density (uses the mclapply function from the parallel package). For Windows machines, a value > 1 will have no effect, see mclapply help for details.
...	additional arguments passed to post.

Value

A list with entries:
samp: Matrix containing sampled values
w: Vector of weights for values in samp
normconst: normalization constant estimated based on importance sampling
ESS: Effective sample size (for IS)
accept: Acceptance rate (for IMH)

Author(s)

Bjoern Bornkamp

Examples

  ## see function iterLap for an example on how to use IS and IMH

iterLap documentation built on Oct. 1, 2023, 1:06 a.m.