mcmc_condpoisson_cc: Performs MCMC sampling for mixtures of conditional Poisson...

In simonsays1980/finmix: An R package for Bayesian estimation of finite mixture distributions

Description

For internal usage only. This function gets passed the fdata, model, prior, mcmc objects to perform MCMC sampling for a conditional Poisson mixture model. In addition an mcmcoutput object is given that stores the output of MCMC sampling in R memory. Note that finmix relies in C++ code on so-called "mixin" layers that help to design a software by organizing code into layers that build upon each others and enable modularity in MCMC sampling by allowing to combine different sampling designs, e.g. with or without a hierarchical prior, with fixed indicators or storing posterior density parameters. See for more information on mixin layers Smaragdakis and Butory (1998).

Usage

mcmc_condpoisson_cc(data_S4, model_S4, prior_S4, mcmc_S4, mcmcoutput_S4)

Arguments

data_S4	An fdata object storing the observations and indicators.
model_S4	A model object specifying the conditional Poisson finite mixture model.
prior_S4	A prior object specifying the prior distribution for MCMC sampling.
mcmc_S4	A mcmc object specifying the hyper-parameters for MCMC sampling.
mcmcoutput_S4	An mcmcoutput object storing the outcomes from MCMC sampling using R memory.

Value

An mcmcoutput object containing the results from MCMC sampling and using the R memory from the input argument mcmcoutput_S4.

References

• Smaragdakis, Y. and Butory, D. (1998), "Implementing layered designs with mixin layers." In: Jul E. (eds) ECOOP’98 — Object-Oriented Programming. ECOOP 1998. Lecture Notes in Computer Science, vol 1445. Springer, Berlin, Heidelberg.

See Also

• mixturemcmc() for performing MCMC sampling

• fdata for the fdata class definition

• model for the model class definition

• prior for the prior class definition

• mcmc for the mcmc class definition

simonsays1980/finmix documentation built on Dec. 23, 2021, 2:25 a.m.