vmax {TargetScore} | R Documentation |
The M step in VB-EM iteration.
vmax(X, model, prior)
X |
D x N numeric vector or matrix of N observations (columns) and D variables (rows) |
model |
List containing model parameters (see |
prior |
List containing the hyperparameters defining the prior distributions |
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). |
X is expected to be D x N for N observations (columns) and D variables (rows)
Yue Li
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)
X <- c(rnorm(100,mean=2), rnorm(100,mean=3)) tmp <- vbgmm(X, tol=1e-3) names(tmp$full.model)