BNSamplerBigFlips: Big flips MCMC sampler for Bayesian networks.

In rjbgoudie/structmcmc: Structural inference for Bayesian Networks and Variable Selection.

Description

Create a MCMC sampler for Bayesian Networks. The sampler samples Bayesian Networks (ie models).

Usage

  BNSamplerBigFlips(data, initial, prior,
    return = "network", logScoreFUN = logScoreMultDirFUN(),
    logScoreParameters = list(hyperparameters = "bdeu"),
    verbose = F, keepTape = F)

Arguments

data	The data.
initial	An object of class 'bn'. The starting value of the MCMC.
prior	A function that returns the prior score of the supplied bn.
return	Either "network" or "contingency".
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.
logScoreParameters	...
verbose	A logical of length 1, indicating whether verbose output should be printed.
keepTape	A logical of length 1, indicating whether a full log ('tape') of the MCMC sampler should be kept. Enabling this option can be very memory-intensive.

Details

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Value

A function, which when called draws the next sample of the MCMC.

See Also

BNSampler, BNSamplerPT, BNSamplerMJ, BNSamplerGrzeg

rjbgoudie/structmcmc documentation built on Nov. 3, 2020, 3:41 a.m.