sensitivity: Sensitivity function

In bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

Sensitivity function

Description

sensitivity returns the sensitivity function for a probabilistic query of interest with respect to a parameter change defined by the user.

Usage

sensitivity(
  bnfit,
  interest_node,
  interest_node_value,
  evidence_nodes = NULL,
  evidence_states = NULL,
  node,
  value_node,
  value_parents,
  new_value,
  covariation = "proportional"
)

Arguments

bnfit	object of class bn.fit.
interest_node	character string. Node of the probability query of interest.
interest_node_value	character string. Level of interest_node.
evidence_nodes	character string. Evidence nodes. If NULL no evidence is considered. Set by default to NULL.
evidence_states	character string. Levels of evidence_nodes. If NULL no evidence is considered. If evidence_nodes="NULL", evidence_states should be set to NULL. Set by default to NULL.
node	character string. Node of which the conditional probability distribution is being changed.
value_node	character string. Level of node.
value_parents	character string. Levels of node's parents. The levels should be defined according to the order of the parents in bnfit[[node]][["parents"]]. If node has no parents, then should be set to NULL.
new_value	numeric vector with elements between 0 and 1. Values to which the parameter should be updated. It can take a specific value or more than one. For more than one value, these should be defined through a vector with an increasing order of the elements. new_value can also take as value the character string all: in this case a sequence of possible parameter changes ranging from 0.05 to 0.95 is considered.
covariation	character string. Co-variation scheme to be used for the updated Bayesian network. Can take values uniform, proportional, orderp, all. If equal to all, uniform, proportional and order-preserving co-variation schemes are considered. Set by default to proportional.

Details

The Bayesian network on which parameter variation is being conducted should be expressed as a bn.fit object. The name of the node to be varied, its level and its parent's level should be specified. The parameter variation specified by the function is:

P ( node = value_node | parents = value_parents ) = new_value

and the probabilistic query of interest is:

P ( interest_node = interest_node_value | evidence_nodes = evidence_states )

Value

A dataframe with the varied parameter values and the output probabilities for the co-variation schemes selected. If plot = TRUE the function also returns a plot of the sensitivity function.

References

Coupé, V. M., & Van Der Gaag, L. C. (2002). Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36(4), 323-356.

Leonelli, M., Goergen, C., & Smith, J. Q. (2017). Sensitivity analysis in multilinear probabilistic models. Information Sciences, 411, 84-97.

See Also

covariation, sensquery

bnmonitor documentation built on June 7, 2023, 5:19 p.m.