NormalInverseWishartPrior

The mean of the prior distribution.  This is
  $\mu0$ in the description above.

mean.guess

The number of observations worth of weight
 assigned to <code>mean.guess</code>. This is $\kappa$ in the
 description above.

mean.guess.weight

A prior estimate at the value of $\Sigma$.
    This is $S^{-1}/\nu$ in the notation above.

variance.guess

The number of observations worth of weight
 assigned to <code>variance.guess</code>. This is $df$.

variance.guess.weight

The NormalInverseWishartPrior is the conjugate prior for the
 mean and variance of the multivariate normal distribution. The model says
 that
 $$\Sigma^{-1} \sim Wishart(\nu, S) \mu|\sigma \sim N(\mu_0, \Sigma/\kappa)$$ The $Wishart(S, \nu)$ distribution is parameterized by <code>S</code>,
 the inverse of the sum of squares matrix, and the scalar
 degrees of freedom parameter <code>nu</code>. The distribution is improper if $\nu < dim(S)$.

normal.inverse.wishart.prior

A C++ library for Bayesian modeling, with an emphasis on
Markov chain Monte Carlo.  Although boom contains a few R utilities
(mainly plotting functions), its primary purpose is to install the
BOOM C++ library on your system so that other packages can link
against it.

normal.inverse.wishart.prior: Normal inverse Wishart prior

Description

Usage

Arguments

References