AM_emp_bayes_uninorm {AntMAN}

compute the hyperparameters of an Normal-Inverse-Gamma distribution using an empirical Bayes approach

Description

This function computes the hyperparameters of a Normal Inverse-Gamma distribution using an empirical Bayes approach. More information about how these hyperparameters are determined can be found here: Bayes and empirical Bayes: do they merge? (Petrone et al. 2012).

Usage

AM_emp_bayes_uninorm(y, scEmu = 1, scEsig2 = 3, CVsig2 = 3)

Arguments

y

The data y. If y is univariate, a vector is expected. Otherwise, y should be a matrix.

scEmu

a positive value (default=1) such that marginally E(\mu) = s^2*scEmu, where s^2 is the sample variance.

scEsig2

a positive value (default=3) such that marginally E(\sigma^2) = s^2*scEsig2, where s^2 is the sample variance.

CVsig2

The coefficient of variation of \sigma^2 (default=3).

Value

an object of class AM_mix_hyperparams, in which hyperparameters m0, k0, nu0 and sig02 are specified. To understand the usage of these hyperparameters, please refer to AM_mix_hyperparams_uninorm.