BayesDecisionBoundaries {AdaptGauss}

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