approximate_inference

data.table object with the TS data

size

obj_vars

starting point in the data set to forecast.

number of repetitions to be performed of the approximate inference

number of particles to be used by bnlearn

num_p

Given a bn.fit object, the size of the net and a data.set,
performs approximate forecasting with bnlearns cpdist function over the 
initial evidence taken from the data set.

Learning and inference over dynamic Bayesian networks of arbitrary
Markovian order. Extends some of the functionality offered by the 'bnlearn'
package to learn the networks from data and perform exact inference.
It offers three structure learning algorithms for dynamic Bayesian networks
and the possibility to perform forecasts of arbitrary length. A tool for
visualizing the structure of the net is also provided via the 'visNetwork'
package.

approximate_inference: Performs approximate inference forecasting with the GDBN over a data set

Description

Usage

Arguments

Value