AM_mix_components_prior_dirac

<p>Fixed value  \(M^*\) for the number of components.</p>

Mstar

<p>Generate a configuration object that assigns a Point mass prior to the number of mixture components.
This is the simplest option and it requires users to specify a value \(M^*\)
such that \(Pr(M=M^* =1\).</p>

Prior

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_mix_components_prior_dirac function

Generate a configuration object that contains a Point mass prior — AM_mix_components_prior_dirac

Generate a configuration object that contains a Point mass prior

AM_mix_components_prior_dirac: Generate a configuration object that contains a Point mass prior

Description

Usage

Arguments

Value

See Also

Examples