AntMAN {AntMAN} | R Documentation |

AntMAN: Anthology of Mixture ANalysis tools AntMan is an R package fitting Finite Bayesian Mixture models with a random number of components. The MCMC algorithm behind AntMAN is based on point processes and offers a more computationally efficient alternative to the 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 on the prior on the number of components: Shifted Poisson, Negative Binomial, and Point Masses (i.e. mixtures with fixed number of components).

The main function of the AntMAN package is `AM_mcmc_fit`

. AntMAN performs a Gibbs sampling in order to fit,
in a Bayesian framework, a mixture model of a predefined type `mix_kernel_hyperparams`

given a sample `y`

.
Additionally AntMAN allows the user to specify a prior on the number of components `mix_components_prior`

and on the weights `mix_weight_prior`

of the mixture.
MCMC parameters `mcmc_parameters`

need to be given as argument for the Gibbs sampler (number of interations, burn-in, ...).
Initial values for the number of clusters (`init_K`

) or a specific clustering allocation (`init_clustering`

) can also be user-specified.
Otherwise, by default, we initialise each element of the sample `y`

to a different cluster allocation. This choice can be computationally inefficient.

For example, in order to identify clusters over a population of patients given a set of medical assumptions:

mcmc = AM_mcmc_parameters(niter=20000) mix = AM_mix_hyperparams_multiber () fit = AM_mcmc_fit (mix, mcmc) summary (fit)

In this example `AM_mix_hyperparams_multiber`

is one of the possible mixtures to use.

AntMAN currently support four different mixtures :

AM_mix_hyperparams_unipois(alpha0, beta0) AM_mix_hyperparams_uninorm(m0, k0, nu0, sig02) AM_mix_hyperparams_multiber(a0, b0) AM_mix_hyperparams_multinorm(mu0, ka0, nu0, Lam0)

Additionally, three types of kernels on the prior number of components are available:

AM_mix_components_prior_pois() AM_mix_components_prior_negbin() AM_mix_components_prior_dirac()

For example, in the context of image segmentation, if we know that there are 10 colours present, a prior dirac can be used :

mcmc = AM_mcmc_parameters(niter=20000) mix = AM_mix_hyperparams_multinorm () prior_component = AM_mix_components_prior_dirac(10) # 10 colours present fit = AM_mcmc_fit (mix, prior_component, mcmc) summary (fit)

[Package *AntMAN* version 1.1.0 Index]