said

<p>Usage frequency of the word "said" in the Brown corpus</p>

datasets

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

said function

<p>A list with 500 observations on the frequency of said in different texts.</p>

Format

Usage frequency of the word "said" in the Brown corpus — said

Usage frequency of the word "said" in the Brown corpus

said: Usage frequency of the word "said" in the Brown corpus

Description

Usage

Arguments

Format

References

Examples