Description Usage Arguments Value See Also
Use MCMC to approximate the posterior distribution
1 2 3 4 5 6 7 | mcmcposterior(data, sampler = BNSampler,
prior = priorUniform(initial),
logScoreFUN = logScoreMultDirFUN(),
logScoreParameters = list(hyperparameters = "bdeu"),
constraint = NULL, maxNumberParents = NULL,
nSamples = 50000, time = F, nBurnin = 10000,
initial = empty(ncol(data), "bn"), verbose = T)
|
data |
The data. |
sampler |
A BNSampler. eg BNSampler or BNGibbsSampler etc |
prior |
A list of functions of the same length as
|
logScoreFUN |
A list of four elements:
|
logScoreParameters |
A list of parameters that are passed to logScoreFUN. |
constraint |
A matrix of dimension ncol(data) x ncol(data) giving constraints to the sample space. The (i, j) element is 1 if the edge i -> j is required -1 if the edge i -> is excluded. 0 if the edge i -> j is not constrained. The diagonal of constraint must be all 0. |
maxNumberParents |
Integer of length 1. The maximum number of parents of any node. |
nSamples |
The number of samples to be draw. Set
this to |
time |
The number of seconds to spend drawing
samples. Set this to |
nBurnin |
The number of samples to discard from the beginning of the sample. |
initial |
An object of class 'bn'. The starting value of the MCMC. |
verbose |
A logical. Should a progress bar be displayed? |
A bnpostmcmc
object.
For more control, use the MCMC sampler directly, e.g.
BNSampler
. See also
posterior
. Example priors
priorGraph
, priorUniform
.
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