SAmix: Graphical representation of the simulated annealing sequence...

In mcsm: Functions for Monte Carlo Methods with R

Description

This function implements a simulated annealing algorithm to optimize the posterior distribution of a normal mixture with two components and only the means unknown,

\code{like=function(mu){ -sum(log((.25*dnorm(da-mu[1])+.75*dnorm(da-mu[2]))))} }

with a schedule temp=1/log(1+t).

Usage

SAmix(x, tolerance = 10^(-4), factor = 1)

Arguments

x	two-dimensional vector, starting point of the simulated annealing algorithm
tolerance	maximal difference in the target value needed to stop the simulated annealing algorithm
factor	scale factor of temp that determines the scale of the random walk

Value

theta	sequence of points explored by the simulated annealing algorithm
like	corresponding sequence of posterior values
ite	number of iterations to reach stable value

Author(s)

Christian P. Robert and George Casella

References

From Chapter 5 of EnteR Monte Carlo Statistical Methods

Examples

da=sample(rbind(rnorm(10^2),2.5+rnorm(3*10^2)))
SAres=SAmix(x=c(-.3,.6),tol=10^(-2),fac=.1)

mcsm documentation built on May 2, 2019, 10:16 a.m.