generate_random_network_exp

number of desired variables per time-slice

n_vars

size

minimum mean allowed for the variables

min_mu

maximum mean allowed for the variables

max_mu

minimum standard deviation allowed for the variables

min_sd

maximum standard deviation allowed for the variables

max_sd

minimum coefficient allowed for the parent nodes

min_coef

maximum coefficient allowed for the parent nodes

max_coef

seed

This function generates both a random DBN and a dataset that can be used to 
learn its structure from data. It's intended for experimental use.

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.

generate_random_network_exp: Experimental function that generates a random DBN and samples a dataset that defines it

Description

Usage

Arguments

Value