Expectation-Maximization algorithm to calculate optimal Gaussian Mixture Model for given data in one Dimension.

1 | ```
EMGauss(Data, Means, SDs,Weights, MaxNumberofIterations,fast)
``` |

`Data` |
vector of data points |

`Means` |
vector(1:L), Means of Gaussians, L == Number of Gaussians |

`SDs` |
estimated Gaussian Kernels = standard deviations |

`Weights` |
optional, relative number of points in Gaussians (prior probabilities): sum(Weights) ==1, default weight is 1/L |

`MaxNumberofIterations` |
Optional, Number of Iterations; default=10 |

`fast` |
Default: FALSE: Using mclust's EM see function |

No adding or removing of Gaussian kernels. Number of Gaussian hast to be set by the length of the vector of Means, SDs and Weights.
This EM is only for univariate data. For multivariate data see package `mclust`

List with

`Means` |
means of GMM generated by EM algorithm |

`SDs` |
standard deviations of GMM generated by EM algorithm |

`Weights` |
prior probabilities of Gaussians |

Onno Hansen-Goos, Michael Thrun

Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006, p 435 ff

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.