Variational-Maximimization in VB-EM (Internal function)

Description

The M step in VB-EM iteration.

Usage

1
vmax(X, model, prior)

Arguments

X

D x N numeric vector or matrix of N observations (columns) and D variables (rows)

model

List containing model parameters (see vbgmm)

prior

List containing the hyperparameters defining the prior distributions

Value

model

A list containing the updated model parameters including alpha (Dirichlet), m (Gaussian mean), kappa (Gaussian variance), v (Wishart degree of freedom), M (Wishart precision matrix).

Note

X is expected to be D x N for N observations (columns) and D variables (rows)

Author(s)

Yue Li

References

Mo Chen (2012). Matlab code for Variational Bayesian Inference for Gaussian Mixture Model. http://www.mathworks.com/matlabcentral/fileexchange/35362-variational-bayesian-inference-for-gaussian-mixture-model

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Information Science and Statistics. NY, USA. (p474-486)

See Also

vbgmm

Examples

1
2
3
X <- c(rnorm(100,mean=2), rnorm(100,mean=3))
tmp <- vbgmm(X, tol=1e-3)
names(tmp$full.model)