mvnmvnp: Bayesian inference on a mutlivariate normal (MVN) mean with a...

In Bolstad: Functions for Elementary Bayesian Inference

Description

Evaluates posterior density for mu, the mean of a MVN distribution, with a MVN prior on mu

Usage

mvnmvnp(y, m0 = 0, V0 = 1, Sigma = NULL, ...)

Arguments

y	a vector of observations from a MVN distribution with unknown mean and known variance-covariance.
m0	the mean vector of the MVN prior, or a scalar constant so that the prior vector of length k with the same element repeated k times, e.g. m0 = 0
V0	the variance-covariance matrix of the MVN prior, or the diagonal of the variance-covariance matrix of the MVN prior, or a scalar constant, say n0, so the prior is n0 * I where I is the k by k identity matrix.
Sigma	the known variance covariance matrix of the data. If this value is NULL, which it is by default, then the sample covariance is used. NOTE: if this is the case then the cdf and quantile functions should really be multivariate t, but they are not - in which case the results are only (approximately) valid for large samples.
...	any other values to be passed to Bolstad.control

Value

A list will be returned with the following components:

mean	the posterior mean of the MVN posterior distribution
var	the posterior variance-covariance matrix of the MVN posterior distribution
cdf	a function that will evaluation the posterior cdf at a given point. This function calls mvtnmorm::pmvnorm.
quantile	a function that will find quantiles from the posterior given input probabilities. This function calls mvtnorm::qmvnorm.

Bolstad documentation built on Jan. 8, 2021, 2:03 a.m.