This function implements MCMC with Dirichlet process prior on a numeric vector.

1 |

`y` |
input numeric vector, can be either sAGP or CCF from one sample. |

`alpha` |
significance level. |

`low.thr` |
values below this threshold in |

`prior` |
a list of prior parameters required for |

`mcmc` |
a list of parameters required to run MCMC for |

Three models are evaluated in this function. 0) There is not enough events (n<5) to evaluate which model is true. 1) Normal-Uniform mixture and 2) Normal mixture with unknown number of Guassian peaks. The first model is evaluated by `SampleNMM()`

, and the second by MCMC fitting. The two models are compared by BIC scores and a P-value is obtained from likelihood ratio test.

A list is returned. In case of model 0, the list contains:

`model` |
always 0 |

In case of model 1, the list contains:

`PA0` |
peak information, always equals -1. |

`A` |
proportion of Uniform component. |

`mu` |
mean of Normal component. |

`sigma` |
standard deviation of Normal component. |

`P` |
P-value |

`model` |
always 1 |

In case of model 2, the list contains:

`PA0` |
peak information |

`x,y` |
density function fitted by MCMC. |

`P` |
P value |

`model` |
always 2. |

Bo Li

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