Posterior estimation for Dirichlet process mixture of multivariate (potentially skew) distibutions models
multivariate skew-t probability density function
EM MLE for mixture of NiW
Post-processing Dirichlet Process Mixture Models results to get
a mixture distribution of the posterior locations
Convergence diagnostic plots
DPMGibbsSkewT_SeqPrior_parallel
Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions
C++ implementation of multivariate Normal inverse Wishart probability density function for multiple inputs
C++ implementation of residual trace computation step used when sampling the scale
EM MAP for mixture of sNiW
Plot of a Dirichlet process mixture of gaussian distribution partition
Point estimate of the partition using a modified Binder loss function
multivariate-Normal probability density function
multivariate Skew-Normal probability density function
Sample from a Wishart distribution
Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions
MLE for Gamma distribution
C++ implementation of multivariate normal likelihood function for multiple inputs
C++ implementation
Point estimate of the partition using the F-measure as the cost function.
Sample from a Normal inverse-Wishart distribution
whose parameter are given by the structure hyper
C++ implementation of multivariate skew Normal probability density function for multiple inputs
Construction of an Empirical based prior
C++ implementation of multivariate skew normal likelihood function for multiple inputs
Compute a limited F-measure
Methods for a summary of a 'DPMMclust' object
Slice Sampling of Dirichlet Process Mixture of skew normal ditributions
Return updated sufficient statistics S for skew t-distribution
with data matrix z
multivariate Normal inverse Wishart probability density function for multiple inputs
Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions
Multivariate log gamma function
Plot of a Dirichlet process mixture of skew normal distribution partition
Multiple cost computations with Fmeasure as the loss function
C++ implementation of multivariate Normal probability density function for multiple inputs
Return updated sufficient statistics S with new data matrix z
C++ implementation of multivariate Normal probability density function for multiple inputs
C++ implementation of the F-measure computation without the ref classe 0
Slice Sampling of the Dirichlet Process Mixture Model
with a prior on alpha
Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions
EM MLE for mixture of sNiW
Bayesian Nonparametrics for Automatic Gating of Flow Cytometry data
Probability density function of multiple structured Normal inverse Wishart
Scatterplot of flow cytometry data
Plot of a Dirichlet process mixture of skew t-distribution partition
C++ implementation of multivariate Normal probability density function for multiple inputs
multivariate Student's t-distribution probability density function
C++ implementation of multivariate Normal probability density function for multiple inputs
Sampler for the concentration parameter of a Dirichlet process
Generating cluster data from the Chinese Restaurant Process
C++ implementation
Gets a point estimate of the partition using posterior expected adjusted
Rand index (PEAR)
Slice Sampling of Dirichlet Process Mixture of Gaussian distibutions
Parallel Implementation of Slice Sampling of Dirichlet Process Mixture of skew Normals
C++ implementation of the F-measure computation
C++ implementation of similarity matrix computation using precomputed distances
ELoss of a partition point estimate compared to a gold standard
C++ implementation of the multinomial sampling from a matrix
of column vectors, each containing the sampling probabilities
for their respective draw
C++ implementation
Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha
Sample from a inverse-Wishart distribution
Burning MCMC iterations from a Dirichlet Process Mixture Model.
C++ implementation of multivariate structured Normal inverse Wishart probability density function for multiple inputs
MLE for sNiW distributed observations
Return updated sufficient statistics S with new data matrix z
C++ implementation of multivariate skew t likelihood function for multiple inputs
Computes the coclustering (or similarity) matrix
Sample from a normal inverse Wishart distribution
whose parameter are given by the structure SufStat
Point estimate of the partition for the Binder loss function
Summarizing Dirichlet Process Mixture Models