C++ implementation of multivariate Normal inverse Wishart probability density function for multiple inputs

Usage

mmNiWpdfC(Mu, Sigma, U_Mu0, U_Kappa0, U_Nu0, U_Sigma0, Log = TRUE)

Arguments

Mu: data matrix of dimension p x n, p being the dimension of the data and n the number of data points, where each column is an observed mean vector.
Sigma: list of length n of observed variance-covariance matrices, each of dimensions p x p.
U_Mu0: mean vectors matrix of dimension p x K, K being the number of distributions for which the density probability has to be evaluated
U_Kappa0: vector of length K of scale parameters.
U_Nu0: vector of length K of degree of freedom parameters.
U_Sigma0: list of length K of variance-covariance matrices, each of dimensions p x p.
Log: logical flag for returning the log of the probability density function. Defaults is TRUE.

Value

matrix of densities of dimension K x n

References

Hejblum BP, Alkhassim C, Gottardo R, Caron F and Thiebaut R (2019) Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data. The Annals of Applied Statistics, 13(1): 638-660. <doi: 10.1214/18-AOAS1209>. <arXiv: 1702.04407>. https://arxiv.org/abs/1702.04407 doi:10.1214/18-AOAS1209