cICS_mv_P: 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 - PRODUCT KERNEL

Description

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

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
k0	vector, scale parameters for the location component
a0	vector, parameters of scale component
b0	vector, parameters of scale component
m1	means of hyperdistribution of m0
s21	variances of hyperdistribution of m0
tau1	shape parameters of hyperdistribution of k0
tau2	rate parameters of hyperdistribution of k0
a1	shape parameters of hyperdistribution of b0
b1	rate parameters of hyperdistribution of b0
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.