Description Usage Arguments Value Author(s) References Examples

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)*.

1 |

`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 |

`theta` |
sequence of points explored by the simulated annealing algorithm |

`like` |
corresponding sequence of posterior values |

`ite` |
number of iterations to reach stable value |

Christian P. Robert and George Casella

From Chapter 5 of **EnteR Monte Carlo Statistical Methods**

1 2 |

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