posteriorDistr.bma: Posterior distribution in the average model

In lbelzile/BMAmevt: Multivariate Extremes: Bayesian Estimation of the Spectral Measure

Posterior distribution in the average model

Description

Builds an empirical distribution defined as a sum of weighted Dirac masses from posterior samples in individual models.

Usage

posteriorDistr.bma(postweights = c(0.5, 0.5), post.distrs = list())

Arguments

postweights	a vector of positive real numbers, summing to one: the posterior weights (in the same order as the elements of post.distrs) of the individual models.
post.distrs	A list of same length as postweights. Each element must be a vector which will be used as a posterior sample.

Value

A matrix with two rows and as many columns as the sum of the lengths of the elements of post.distrs. The second line contains the weighted posterior sample in the BMA; the first line contains the weights to be assigned to each corresponding value on the second one.

lbelzile/BMAmevt documentation built on April 28, 2023, 2:29 p.m.