Stepise forward non-exhaustive greedy search, calculates the optimum value of the discount factor.
stepwise.forward(
Data,
node,
nbf = 15,
delta = seq(0.5, 1, 0.01),
max.break = TRUE,
priors = priors.spec()
)
Dataset with dimension number of time points T
x number of nodes Nn
.
The node to find parents for.
The Log Predictive Likelihood will sum from (and including) this time point.
A vector of values for the discount factor.
If TRUE
, the code will break if adding / removing parents does not
improve the LPL. If FALSE
, the code will continue to the zero parent / all parent model.
Default is TRUE
.
List with prior hyperparameters.
model.store The parents, LPL and chosen discount factor for the subset of models scored using this method.