bnpostmcmc: BN Posterior from MCMC.
In rjbgoudie/structmcmc: Structural inference for Bayesian Networks and Variable Selection.
Description
Usage
Arguments
See Also
Description
method description
Usage
1
bnpostmcmc(sampler, samples, logScoreFUN)
Arguments
sampler
...
samples
...
logScoreFUN
A list of four elements:
- offline
A function that computes the logScore of a
Bayesian Network
- online
A function that
incrementally computes the logScore of a Bayesian
Network
- local
A function that computes the local
logScore of a Bayesian Network
- prepare
A function
that prepares the data, and any further pre-computation
required by the logScore functions.
For
Multinomial-Dirichlet models,
logScoreMultDirFUN returns the appropriate
list; for Normal models with Zellner g-priors,
logScoreNormalFUN returns the appropriate
list.
See Also
bnpost, bnpostmcmc.list.
length.bnpostmcmc,
top.bnpostmcmc,
map.bnpostmcmc,
logScoreMultDir.bnpostmcmc,
gp.bnpostmcmc, ep.bnpostmcmc
rjbgoudie/structmcmc documentation built on Nov. 3, 2020, 3:41 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments See Also
Description
method description
Usage
1 | bnpostmcmc(sampler, samples, logScoreFUN)
|
Arguments
sampler |
... |
samples |
... |
logScoreFUN |
A list of four elements:
For
Multinomial-Dirichlet models,
|
See Also
bnpost, bnpostmcmc.list.
length.bnpostmcmc,
top.bnpostmcmc,
map.bnpostmcmc,
logScoreMultDir.bnpostmcmc,
gp.bnpostmcmc, ep.bnpostmcmc
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.