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normal.inverse.wishart.prior

Normal inverse Wishart prior


Description

The NormalInverseWishartPrior is the conjugate prior for the mean and variance of the multivariate normal distribution. The model says that

Σ^{-1} ~ Wishart(ν, S) μ | Σ ~ N(μ0, Σ/κ)

The Wishart(S, ν) distribution is parameterized by S, the inverse of the sum of squares matrix, and the scalar degrees of freedom parameter nu.

The distribution is improper if ν < dim(S).

Usage

NormalInverseWishartPrior(mean.guess,
                          mean.guess.weight = .01,
                          variance.guess,
                          variance.guess.weight = nrow(variance.guess) + 1)

Arguments

mean.guess

The mean of the prior distribution. This is μ0 in the description above.

mean.guess.weight

The number of observations worth of weight assigned to mean.guess. This is κ in the description above.

variance.guess

A prior estimate at the value of Σ. This is S^{-1}/ν in the notation above.

variance.guess.weight

The number of observations worth of weight assigned to variance.guess. This is df.

Author(s)

References

Gelman, Carlin, Stern, Rubin (2003), "Bayesian Data Analysis", Chapman and Hall.


Boom

Bayesian Object Oriented Modeling

v0.9.7
LGPL-2.1 | file LICENSE
Authors
Steven L. Scott is the sole author and creator of the BOOM project. Some code in the BOOM libraries has been modified from other open source projects. These include Cephes (obtained from Netlib, written by Stephen L. Moshier), NEWUOA (M.J.D Powell, obtained from Powell's web site), and a modified version of the R math libraries (R core development team). Original copyright notices have been maintained in all source files. In these cases, copyright claimed by Steven L. Scott is limited to modifications made to the original code. Google claims copyright for code written while Steven L. Scott was employed at Google from 2008 - 2018, but BOOM is not an officially supported Google project.
Initial release
2021-02-15

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