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sd.prior

Prior for a standard deviation or variance

Description

Specifies an inverse Gamma prior for a variance parameter, but inputs are defined in terms of a standard deviation.

Usage

SdPrior(sigma.guess, sample.size = .01, initial.value = sigma.guess,
          fixed = FALSE, upper.limit = Inf)

Arguments

sigma.guess

A prior guess at the value of the standard deviation.
sample.size

The weight given to sigma.guess. Interpretable as a prior observation count.
initial.value

The initial value of the paramter in the MCMC algorithm.
fixed

Logical. Some algorithms allow you to fix sigma at a particular value. If TRUE then sigma will remain fixed at initial.value, if supported.
upper.limit

If positive, this is the upper limit on possible values of the standard deviation parameter. Otherwise the upper limit is assumed infinite. Not supported by all MCMC algorithms.

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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