Jeffreys

<p>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 constants are used
across other models.</p>

Package for Bayesian Variable Selection and  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)
<DOI:10.1198/016214507000001337>
for linear models or mixtures of g-priors from  Li and Clyde
(2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
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 which
samples models using a tree structure of the model space
as an efficient hash table.  See  Clyde, Ghosh and Littman (2010)
<DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
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 to enforce sampling models that are full rank.
The user may force variables to always be included in addition to imposing
constraints that higher order interactions are included only if their
parents are included in the model.
This material is based upon work supported by the National Science
Foundation under Division of Mathematical Sciences grant 1106891.
Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the
National Science Foundation.

Merlise Clyde

Bayesian Variable Selection and Model Averaging using Bayesian
Adaptive Sampling

Michael Littman

Quanli Wang

Joyee Ghosh

Yingbo Li

Don van de Bergh

Jeffreys function

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

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

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

Description

Usage

Arguments

Value

Details

See Also

Examples