UseBasicPrior {vabayelMix}R Documentation

Prior Function for Variational Gaussian Mixture Model

Description

This function implements an uninformative prior distribution for the cluster centers and variances, but allows the user to define prior weights for the clusters.

Usage

UseBasicPrior(data, weights.v)

Arguments

data A matrix with columns representing variables and rows observations. Algorithm clusters observations.
weights.v A vector of relative prior weights for the clusters.

Details

weights.v is a vector of length Ncat, the maximum number of clusters to look for.

Value

A list with following components. The first four are matrices of dimension Ncat x Ndim, dapi is a vector of length Ncat.

mean the means of the cluster mean gaussian priors.
varm the inverse variances for the cluster mean gaussian priors.
ivara parameters for the gamma prior distribution of the inverse variances of the clusters. See references.
ivarb parameters for the gamma prior distribution of the inverse variances of the clusters. See references.
dapi weight vector specifying prior knowledge about the number of clusters.

Author(s)

Andrew Teschendorffaet21@hutchison-mrc.cam.ac.uk

References

1
D.J.MacKay: Developments in probabilistic modelling with neural networks-ensemble learning. In Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the 3rd Annual Symposium on Neural Networksm Nijmengen, Netherlands, Berlin Springer, 191-198 (1995).
2
J.W.Miskin : Ensemble Learning for Independent Component Analysis, PhD thesis University of Cambridge December 2000.
3
A. E. Teschendorff,...et al.: A variational bayesian mixture modelling framework for cluster analysis of gene expression data. Submitted to Bioinformatics.


[Package vabayelMix version 0.3 Index]