GRApprox: Gelman-Rubin mode approximation

In iterLap: Approximate Probability Densities by Iterated Laplace Approximations

Gelman-Rubin mode approximation

Description

Performs the multiple mode approximation of Gelman-Rubin (applies a Laplace approximation to each mode). The weights are determined corresponding to the height of each mode.

Usage

GRApprox(post, start, grad, method = c("nlminb", "nlm", "Nelder-Mead", "BFGS"),
         control = list(), ...)

Arguments

post	log-posterior density.
start	vector of starting values if dimension=1 otherwise matrix of starting values with the starting values in the rows
grad	gradient of log-posterior
method	Which optimizer to use
control	Control list for the chosen optimizer
...	Additional arguments for log-posterior density specified in post

Value

Produces an object of class mixDist. That a list mit entries
weights Vector of weights for individual components
means Matrix of component medians of components
sigmas List containing scaling matrices
eigenHess List containing eigen decompositions of scaling matrices
dets Vector of determinants of scaling matrix
sigmainv List containing inverse scaling matrices

Author(s)

Bjoern Bornkamp

References

Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (2003) Bayesian Data Analysis, 2nd edition, Chapman and Hall. (Chapter 12)

Bornkamp, B. (2011). Approximating Probability Densities by Iterated Laplace Approximations, Journal of Computational and Graphical Statistics, 20(3), 656–669.

See Also

iterLap

Examples

  ## log-density for banana example
  banana <- function(pars, b, sigma12){
    dim <- 10
    y <- c(pars[1], pars[2]+b*(pars[1]^2-sigma12), pars[3:dim])
    cc <- c(1/sqrt(sigma12), rep(1, dim-1))
    return(-0.5*sum((y*cc)^2))
  }

  start <- rbind(rep(0,10),rep(-1.5,10),rep(1.5,10))
  ## multiple mode Laplace approximation
  aa <- GRApprox(banana, start, b = 0.03, sigma12 = 100)
  ## print mixDist object
  aa
  ## summary method
  summary(aa)
  ## importance sampling using the obtained mixDist object 
  ## using a mixture of t distributions with 10 degrees of freedom
  dd <- IS(aa, nSim=1000, df = 10, post=banana, b = 0.03,
           sigma12 = 100)
  ## effective sample size
  dd$ESS

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