Dirichlet Process Mixture model simulation for clustering and
image segmentation
The package implements a Dirichlet Process mixture (DPM)
model for clustering and image segmentation. The DPM model is a
Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components. The code implements conjugate models with normal
structure (conjugate normal-normal DP mixture model). The
package's applications are oriented towards the classification
of magnetic resonance images according to tissue type or region
of interest.

The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation. The DPM model is a
Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components. The code implements conjugate models with normal
structure (conjugate normal-normal DP mixture model). The
package's applications are oriented towards the classification
of magnetic resonance images according to tissue type or region
of interest.

The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation.  The DPM model is
a Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components.  The code implements conjugate models with
normal structure (conjugate normal-normal DP mixture model).
The package's applications are oriented towards the
classification of magnetic resonance images according to tissue
type or region of interest.

Dirichlet Process Mixture Model Simulation for Clustering and
Image Segmentation
The 'dpmixsim' package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation.  The DPM model is
a Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components.  The code implements conjugate models with
normal structure (conjugate normal-normal DP mixture model).
The package's applications are oriented towards the
classification of magnetic resonance images according to tissue
type or region of interest.