summary.AM_mix_components_prior

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

object

all additional parameters are ignored.

Given an <code><a rd-options="" href="/link/AM_mix_components_prior?package=AntMAN&version=1.1.0" data-mini-rdoc="AntMAN::AM_mix_components_prior">AM_mix_components_prior</a></code> object, this function prints the summary information
of the specified prior on the number of components.

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

summary.AM_mix_components_prior function

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

Given an <code><a rd-options='' href='AM_mix_components_prior'>AM_mix_components_prior</a></code> object, this function prints the summary information
of the specified prior on the number of components.

summary.AM_mix_components_prior: summary information of the AM_mix_components_prior object

Description

Usage

Arguments

Value

See Also