`bpr_fdmm`

implements the Gibbs sampling algorithm for performing
clustering on DNA methylation profiles, where the observation model is the
Binomial distributed Probit Regression function,
`bpr_likelihood`

.

1 2 3 4 |

`x` |
A list of elements of length N, where each element is an L x 3 matrix of observations, where 1st column contains the locations. The 2nd and 3rd columns contain the total trials and number of successes at the corresponding locations, repsectively. |

`K` |
Integer denoting the number of clusters K. |

`pi_k` |
Vector of length K, denoting the mixing proportions. |

`w` |
A MxK matrix, where each column contains the basis function coefficients for the corresponding cluster. |

`basis` |
A 'basis' object. E.g. see |

`w_0_mean` |
The prior mean hyperparameter for w |

`w_0_cov` |
The prior covariance hyperparameter for w |

`dir_a` |
The Dirichlet concentration parameter, prior over pi_k |

`gibbs_nsim` |
Argument giving the number of simulations of the Gibbs sampler. |

`gibbs_burn_in` |
Argument giving the burn in period of the Gibbs sampler. |

`is_parallel` |
Logical, indicating if code should be run in parallel. |

`no_cores` |
Number of cores to be used, default is max_no_cores - 1. |

`is_verbose` |
Logical, print results during EM iterations |

andreaskapou/mpgex documentation built on Nov. 25, 2017, 8:08 a.m.

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