cDDP: C++ function to estimate DDP models with 1 grouping variables

In BNPmix: Bayesian Nonparametric Mixture Models

C++ function to estimate DDP models with 1 grouping variables

Description

C++ function to estimate DDP models with 1 grouping variables

Arguments

data	a vector of observations.
group	group allocation of the data.
ngr	number of groups.
grid	vector to evaluate the density.
niter	number of iterations.
nburn	number of burn-in iterations.
m0	expectation of location component.
k0	tuning parameter of variance of location component.
a0	parameter of scale component.
b0	parameter of scale component.
mass	mass of Dirichlet process.
wei	prior weight of the specific processes.
b	tuning parameter of weights distribution
napprox	number of approximating values.
n_approx_unif	number of approximating values of the importance step for the weights updating.
nupd	number of iterations to show current updating.
out_dens	if TRUE, return also the estimated density (default TRUE).
print_message	print the status.
light_dens	if TRUE return only the posterior mean of the density

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