Collapsed Variational Inference for Dirichlet Process (DP) Mixture Model
cum_clustprop_var
cum_clustprop
Collapsed variational inference for non-parametric Bayesian mixture mo...
Update of the variational parameters
Root for a0 hyper-parameter for Sparse DPMM
ELBO calculating functions depending on type of model for covariance m...
General ELBO function
Generate random log Probability matrix if not provided
mat_mult_t
mat_mult
Function to check the list of type-specific arguments
S3 plotting function for CVIoutputobjects'
quadratic_form_diag
CVI implementation for one set of initial parameters
sweep_3D
t_mat_mult
Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.