IAM_VnkDelta

<p>The number of component of the mixture</p>

Mstar

<p>The <code>gamma</code> parameter of the Dirichlet</p>

gamma

internal

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

IAM_VnkDelta function

Compute the value V(n,k), needed to caclulate the eppf of a Finite Dirichlet process when the prior on the component-weigts
of the mixture is a Dirichlet with parameter <code>gamma</code> (i.e. when unnormailized weights are distributed as Gamma(\(\gamma\),1) )
when the number of component are fixed to <code>M^*</code>, i.e. a Dirac prior assigning mass only to <code>M^*</code> is assumed.
See Section 9.1.1 of the Paper Argiento de Iorio 2019 for more details. — IAM_VnkDelta

Compute the value V(n,k), needed to caclulate the eppf of a Finite Dirichlet process when the prior on the component-weigts
of the mixture is a Dirichlet with parameter <code>gamma</code> (i.e. when unnormailized weights are distributed as Gamma(\(\gamma\),1) )
when the number of component are fixed to <code>M^*</code>, i.e. a Dirac prior assigning mass only to <code>M^*</code> is assumed.
See Section 9.1.1 of the Paper Argiento de Iorio 2019 for more details.

Description

Usage

Arguments

Value