Bayes4Mixtures {AdaptGauss} | R Documentation |

## Posterioris of Bayes Theorem

### Description

Calculates the posterioris of Bayes theorem

### Usage

```
Bayes4Mixtures(Data, Means, SDs, Weights, IsLogDistribution,
PlotIt, CorrectBorders,Color,xlab,lwd)
```

### Arguments

`Data` |
vector (1:N) of data points |

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

`SDs` |
vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means |

`Weights` |
vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means |

`IsLogDistribution` |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length L |

`PlotIt` |
Optional, Default: FALSE; TRUE do a Plot |

`CorrectBorders` |
Optional, ==TRUE data at right borders of GMM distribution will be assigned to last gaussian, left border vice versa. (default ==FALSE) normal Bayes Theorem |

`Color` |
Optional, character vector of colors, default rainbow() |

`xlab` |
Optional, label of x-axis, default 'Data', see intern R documentation |

`lwd` |
Width of Line, see intern R documentation |

### Details

See conference presentation for further explanation.

### Value

List with

`Posteriors` |
(1:N,1:L) of Posteriors corresponding to Data |

`NormalizationFactor` |
(1:N) denominator of Bayes theorem corresponding to Data |

### Author(s)

Catharina Lippmann, Onno Hansen-Goos, Michael Thrun

### References

Thrun M.C.,Ultsch, A.: Models of Income Distributions for Knowledge Discovery, European Conference on Data Analysis, DOI 10.13140/RG.2.1.4463.0244, Colchester 2015.

### See Also

`BayesDecisionBoundaries`

,`AdaptGauss`

*AdaptGauss*version 1.6 Index]