A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.
|Author||Kaniav Kamary, Kate Lee|
|Date of publication||2017-03-09 00:33:27|
|Maintainer||Kaniav Kamary <firstname.lastname@example.org>|
|License||GPL (>= 2.0)|
K.MixPois: Sample from a Poisson mixture posterior associated with a...
K.MixReparametrized: Sample from a Gaussian mixture posterior associated with a...
Plot.MixReparametrized: plot of the MCMC output produced by K.MixReparametrized
SM.MAP.MixReparametrized: summary of the output produced by K.MixReparametrized
SM.MixPois: summary of the output produced by K.MixPois
SM.MixReparametrized: summary of the output produced by K.MixReparametrized
Ultimixt-package: set of R functions for estimating the parameters of mixture...
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