Given a N x D matrix of N observations and D variables, compute VB-GMM via VB-EM.

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`data` |
N x D numeric vector or matrix of N observations (rows) and D variables (columns) |

`init` |
Based on the dimension, init is expected to be one of the followings: scalar: number of components; vector: intial class labels; matrix: initialize with a D x K matrix for D variables and K components. |

`prior` |
A list containing the hyperparameters including alpha (Dirichlet), m (Gaussian mean), kappa (Gaussian variance), v (Wishart degree of freedom), M (Wishart precision matrix). |

`tol` |
Threshold that defines termination/convergence of VB-EM when abs(L[t] - L[t-1])/abs(L[t]) < tol |

`maxiter` |
Scalar for maximum number of EM iterations |

`mirprior` |
Boolean to indicate whether to use expectedTargetFreq to initialize alpha0 for the hyperparameters of Dirichlet. |

`expectedTargetFreq` |
Expected target frequence within the gene population. By default, it is set to 0.01, which is consistent with the widely accepted prior knoweldge that 200/20000 targets per miRNA. |

`verbose` |
Boolean indicating whether to show progress in terms of lower bound ( |

The function implements variation Bayesian multivariate GMM described in Bishop (2006). Please refer to the reference below for more details. This is the workhorse of `targetScore`

. Alternatively, user can choose to apply this function to other problems other than miRNA target prediction.

A list containing:

`label` |
a vector of maximum-a-posteriori (MAP) assignments of latent discrete values based on the posteriors of latent variables. |

`R` |
N x D matrix of posteriors of latent variables |

`mu` |
Gaussian means of the latent components |

`full.model` |
A list containing posteriors R, logR, and the model parameters including alpha (Dirichlet), m (Gaussian mean), kappa (Gaussian variance), v (Wishart degree of freedom), M (Wishart precision matrix) |

`L` |
A vector of variational lower bound at each EM iterations (should be strictly increasing) |

Yue Li

Mo Chen (2012). Matlab code for Variational Bayesian Inference for Gaussian Mixture Model. http://www.mathworks.com/matlabcentral/fileexchange/35362-variational-bayesian-inference-for-gaussian-mixture-model

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Information Science and Statistics. NY, USA. (p474-486)

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