Jeffreys

Creates an object
  representing the Jeffrey's Prior on g mixture of g-priors on
  coefficients for BAS. This is equivalent to a limiting version of the
  CCH(a, 2, 0) with a = 0 or they hyper-g(a = 2) and is an improper
  prior.  As $g$ does not appear in the Null Model, Bayes Factors and
  model probabilities are not well-defined because of arbitrary
  normalizing constants, and for this reason the null model is excluded
  and the same c onstants are used across other models.  

Package for Bayesian Model Averaging in linear models and
generalized linear models using stochastic or
deterministic sampling without replacement from posterior
distributions.  Prior distributions on coefficients are
from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the
mixture of g-priors from
Liang et al (2008)
<http://dx.doi.org/10.1198/016214507000001337>
for linear models or mixtures of
g-priors in GLMs of Li and Clyde (2015)
<http://arxiv.org/abs/1503.06913>. Other model
selection criteria include AIC, BIC and Empirical Bayes estimates of g.
Sampling probabilities may be updated based on the sampled models
using Sampling w/out Replacement or an efficient MCMC algorithm
samples models using the BAS tree structure as an efficient
hash table. Uniform priors over all models or beta-binomial prior distributions on
model size are allowed, and for large p truncated priors on the model
space may be used.  The user may force variables to always be included.
Details behind the sampling algorithm are provided in
Clyde, Ghosh and Littman (2010) <http://dx.doi.org/10.1198/jcgs.2010.09049>.

Jeffreys: Jeffreys Prior Distribution for $g$ for Mixtures of g-Priors for Coefficients in BMA Models

Description

Usage

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Details

See Also