BayesClassification

Bayes Klassifikation den Daten zuordnen

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.

Michael Thrun

AdaptGauss

Gaussian Mixture Models (GMM)

Onno Hansen-Goos

Rabea Griese

Catharina Lippmann

Florian Lerch

Quirin Stier

Jorn Lotsch

Luca Brinkmann

Alfred Ultsch

BayesClassification function

<dl><dt>Data</dt>
<dd>vector of Data</dd>
<dt>Means</dt>
<dd>vector[1:L] of Means of Gaussians (of GMM)</dd>
<dt>SDs</dt>
<dd>vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means</dd>
<dt>Weights</dt>
<dd>vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means</dd>
<dt>IsLogDistribution</dt>
<dd>Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L</dd>
<dt>ClassLabels</dt>
<dd>Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model</dd></dl>

Arguments

Author

BayesClassification — BayesClassification

<dl>

<dt>Data</dt>
<dd>vector of Data</dd>


<dt>Means</dt>
<dd>vector[1:L] of Means of Gaussians (of GMM)</dd>


<dt>SDs</dt>
<dd>vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means</dd>


<dt>Weights</dt>
<dd>vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means</dd>


<dt>IsLogDistribution</dt>
<dd>Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L</dd>


<dt>ClassLabels</dt>
<dd>Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model</dd>

</dl>

BayesClassification: BayesClassification

Description

Usage

Value

Arguments

Author