Variational Bayesian inference for unsupervised clustering, mixture of univariate Gaussians

vimixUniGauss(X, K, prior, init = "kmeans", tol = 1e-19,
maxiter = 2000, verbose = F)

## Arguments

X NxD data matrix. (Maximum) number of clusters. Prior parameters (optional). Initialisation method (optional). If it is a vector, it is interpreted as the vector of initial cluster allocations. If it is a string, it is interpreted as the name of the clustering algorithm used for the initialisation (only "kmeans" and "random") available at the moment). Tolerance on lower bound. Default is 10e-20. Maximum number of iterations of the VB algorithm. Default is 2000. Boolean flag which, if TRUE, prints the iteration numbers. Default is FALSE.

## Value

A list containing L, the lower bound at each step of the algorithm, label, a vector containing the cluster labels, model, a list containing the trained model structure.

## References

Bishop, C.M., 2006. Pattern recognition and machine learning. Springer.

## Examples

data <- c(rnorm(100, -4), rnorm(100, 4))
output <- vimixUniGauss(data, 3)