Graphical representation of the simulated annealing sequence for the mixture posterior

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

1
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

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

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.