AM_mix_weights_prior

<p>Object type returned by <code>AM_mix_weights_prior_*</code> commands.</p>

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_weights_prior function

S3 class AM_mix_weights_prior — AM_mix_weights_prior

S3 class AM_mix_weights_prior

AM_mix_weights_prior: S3 class AM_mix_weights_prior

Description

Arguments

Value

See Also