plot.AM_mcmc_output

an <code><a rd-options="" href="/link/AM_mcmc_output?package=AntMAN&version=1.1.0" data-mini-rdoc="AntMAN::AM_mcmc_output">AM_mcmc_output</a></code> object.

all additional parameters are ignored.

Given an <code><a rd-options="" href="/link/AM_mcmc_output?package=AntMAN&version=1.1.0" data-mini-rdoc="AntMAN::AM_mcmc_output">AM_mcmc_output</a></code> object, this function plots some useful information about the MCMC results
regarding \(M\) and \(K\). Besides the PMFs, some of the diagnostic plots of the MCMC chain are visualised.

Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).

Bruno Bodin

AntMAN

Anthology of Mixture Analysis Tools

Priscilla Ong

Raffaele Argiento

Maria De Iorio

plot.AM_mcmc_output function

an <code><a rd-options='' href='AM_mcmc_output'>AM_mcmc_output</a></code> object.

Given an <code><a rd-options='' href='AM_mcmc_output'>AM_mcmc_output</a></code> object, this function plots some useful information about the MCMC results
regarding \(M\) and \(K\). Besides the PMFs, some of the diagnostic plots of the MCMC chain are visualised.

plot.AM_mcmc_output: plot AM_mcmc_output

Description

Usage

Arguments

Value