sampling_a: MCMC sampling of parameter "a" in the _mixdpcluster_ model...

In BNPMIXcluster: Bayesian Nonparametric Model for Clustering with Mixed Scale Variables

Description

Generates a sample from the posterior distribution of a in the mixdpcluster model for bayesian clustering. The simulation is done via Metropolis-Hastings method.

Usage

sampling_a(
  n = 1,
  a.ini,
  b,
  alpha,
  d_0_a,
  d_1_a,
  mu_star_n_r,
  n.burn = 0,
  n.thin = 0,
  max.time = Inf,
  verbose = F,
  USING_CPP = TRUE
)

Arguments

n	number of simulations to generate
a.ini	initialization value
b	parameter b in the posterior distribution of a
alpha	parameter α in the posterior distribution of a
d_0_a	parameter d_0^a in the posterior distribution of a
d_1_a	parameter d_1^a in the posterior distribution of a
mu_star_n_r	vector with number of observations allocated to each cluster
n.burn	number of iterations in the simulation considered in the burn-in period.
n.thin	number of iterations discarded between two simulated values (for thinning of the MCMC chain).
max.time	maximum allowed time for the simulation process. The function returns Error if exceeded.
verbose	if T, the function reports extra information on progress.
USING_CPP	indicates usage of C++ in some modules.

Value

A list with two elements:

$a.chain

A numeric vector with the simulated values from the posterior distribution of a

$accept.indic

A numeric vector with the simulated values from the posterior distribution of a

References

Carmona C., Nieto-Barajas L., Canale A. (2017). Model based approach for household clustering with mixed scale variables.

BNPMIXcluster documentation built on Nov. 30, 2020, 5:07 p.m.