bnsens | R Documentation |
The package performs sensitivity analysis for discrete Bayesian networks.
It provides three categories of functions:covariation schemes, dissimilarity measures and sensitivity related functions.
The co-variation schemes available are uniform-covariation scheme, proportional co-variation scheme and order-preserving co-variation scheme.
The dissimilarity measures available are the Chan-Darwiche distance and the Kullback-Leibler divergence. The available sensitivity related functions are the standard sensitivity function and sensquery, a new function
which allows to determine the parameter changes that are needed to get a certain probabilistic query of interest.
The available co-variation schemes are:
Uniform co-variation scheme (uniform_covar
): distributes the probability mass to be co-varied uniformly among the co-varying parameters.
Proportional co-variation scheme (proportional_covar
): distributes the probability mass to be co-varied in the same proportion as in the original Bayesian network.
Order-preserving co-variation scheme (orderp_covar
):distributes the to be co-varied probability mass among the co-varying parameters so that the original order of parameters is preserved.
The dissimilarity measures quantify the difference between a Bayesian network and its update after parameter variation.
The available dissimilarity measures are:
Chan-Darwiche distance (CD_distance
)
Kullback-Leibler divergence (KL
)
The available function for sensitivity analysis are:
Sensitivity function (sensitivity
): returns a certain probability of interest given a parameter change. Evidence can be considered.
Sensitivity query (sensquery
): returns the parameter changes needed to get a certain probability of interest. Evidence can be considered.
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