fit_dirichlet: Estimate Parameters of Dirichlet Distribution

In MCMCprecision: Precision of Discrete Parameters in Transdimensional MCMC

Description

C++ implementation of the fixed-point iteration algorithm by Minka (2000).

Usage

fit_dirichlet(x, const, maxit = 1e+05, abstol = 0.1)

Arguments

x	a matrix of Dirichlet samples, one row per observation.
const	constant that is added to avoid problems with zeros in log(x). The default is const = min(x[x>0])*.01.
maxit	maximum number of iterations.
abstol	The absolute convergence tolerance: maximum of absolute differences of Dirichlet parameters.

Details

The algorithm is used to estimate the effective sample size based on samples of posterior model probabilities (see stationary and summary.stationary).

References

Minka, T. (2000). Estimating a Dirichlet distribution. Technical Report.

See Also

rdirichlet

Examples

x <- rdirichlet(100, c(8,1,3,9))
fit_dirichlet(x)

MCMCprecision documentation built on Dec. 5, 2019, 9:08 a.m.