normalizelogweights

Compute normalized probabilities from unnormalized
 log-probabilities.

Fast algorithms for fitting Bayesian variable selection
models and computing Bayes factors, in which the outcome (or
response variable) is modeled using a linear regression or a
logistic regression. The algorithms are based on the variational
approximations described in "Scalable variational inference for
Bayesian variable selection in regression, and its accuracy in
genetic association studies" (P. Carbonetto & M. Stephens, 2012,
<DOI:10.1214/12-BA703>). This software has been applied to large
data sets with over a million variables and thousands of samples.

Peter Carbonetto

varbvs

Large-Scale Bayesian Variable Selection Using Variational
Methods

Matthew Stephens

David Gerard

normalizelogweights function

<dl> <dt>logw</dt>
<dd>Vector of unnormalized log-probabilities.</dd></dl>

Arguments

Peter Carbonetto <a href="/link/peter.carbonetto%40gmail.com?package=varbvs&version=2.6-10" data-mini-rdoc="varbvs::peter.carbonetto@gmail.com">peter.carbonetto@gmail.com</a>

Author

Compute normalized probabilities. — normalizelogweights

<dl>

 <dt>logw</dt>
<dd>Vector of unnormalized log-probabilities.</dd>

</dl>

Peter Carbonetto <a href='mailto:peter.carbonetto@gmail.com'>peter.carbonetto@gmail.com</a>

normalizelogweights: Compute normalized probabilities.

Description

Usage

Value

Arguments

Author

Details

References

Examples