cICS_mv_L: C++ function to estimate Pitman-Yor multivariate mixtures via...

In BNPmix: Bayesian Nonparametric Mixture Models

C++ function to estimate Pitman-Yor multivariate mixtures via importance conditional sampler

Description

C++ function to estimate Pitman-Yor multivariate mixtures via importance conditional sampler

Arguments

data	a matrix of observations
grid	matrix of points to evaluate the density
niter	number of iterations
nburn	number of burn-in iterations
m0	expectation of location component
S20	variance of location component
S0	parameter of scale component
n0	parameter of scale component
m1	mean of hyperdistribtion of m0
k1	scale factor of hyperdistribtion of m0
theta1	df of hyperdistribtion of S20
Theta1	matrix of hyperdistribution of S20
strength	strength parameter
napprox	number of approximating values
nupd	number of iterations to show current updating
out_param	if TRUE, return also the location and scale paramteres lists
out_dens	if TRUE, return also the estimated density (default TRUE)
discount	second parameter of PY
print_message	print the status
light_dens	if TRUE return only the posterior mean of the density
hyper,	if TRUE use hyperpriors, default TRUE

BNPmix documentation built on July 16, 2022, 1:04 a.m.