<p>Function that implements the integrand of the target functional to minimize in order to
obtain hyperparameters that produces optimal estimates in the frequentist context.</p>

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Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Aldo Gardini

BayesLN

Bayesian Inference for Log-Normal Data

g_V: Integral of the target functional to minimize

Description

Usage

Arguments

Details