BayesDecisionBoundaries {AdaptGauss} | R Documentation |

## Decision Boundaries calculated through Bayes Theorem

### Description

Function finds the intersections of Gaussians or LogNormals

### Usage

```
BayesDecisionBoundaries(Means,SDs,Weights,IsLogDistribution,MinData,MaxData,Ycoor)
```

### Arguments

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

`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 1:L |

`MinData` |
Optional, Beginning of range, where the Boundaries are searched for, default min(M) |

`MaxData` |
Optional, End of range, where the Boundaries are searched for, default max(M) |

`Ycoor` |
Optional, Bool, if TRUE instead of vector of DecisionBoundaries list of DecisionBoundaries and DBY is returned |

### Value

`DecisionBoundaries` |
vector[1:L-1], Bayes decision boundaries |

`DBY` |
if (Ycoor==TRUE), y values at the cross points of the Gaussians is also returned, that the return is a list of DecisionBoundaries and DBY |

### Author(s)

Michael Thrun, Rabea Griese

### References

Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. 2nd. Edition. New York, p. 512ff

### See Also

`AdaptGauss`

,`Intersect2Mixtures`

,`Bayes4Mixtures`

*AdaptGauss*version 1.6 Index]