AM_plot_similarity_matrix

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> fit object, produced by calling <code>AM_mcmc_fit</code>.

Loss function to minimise. Specify either "VI" or "binder". If not specified, the default loss
to minimise is "binder".

loss

All additional parameters wil lbe pass to the image command.

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 will produce an image of the Similarity Matrix.

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

AM_plot_similarity_matrix function

An <code><a rd-options='' href='AM_mcmc_output'>AM_mcmc_output</a></code> fit object, produced by calling <code>AM_mcmc_fit</code>.

Given an <code><a rd-options='' href='AM_mcmc_output'>AM_mcmc_output</a></code> object, this function will produce an image of the Similarity Matrix.

AM_plot_similarity_matrix: Plot the Similarity Matrix

Description

Usage

Arguments

Value