covariation {bnmonitor} | R Documentation |
Functions that return an updated Bayesian network using the proportional, uniform and order-preserving co-variation schemes.
proportional_covar(bnfit, node, value_node, value_parents, new_value) orderp_covar(bnfit, node, value_node, value_parents, new_value) uniform_covar(bnfit, node, value_node, value_parents, new_value)
bnfit |
object of class |
node |
character string. Node of which the conditional probability distribution is being changed. |
value_node |
character string. Level of |
value_parents |
character string. Levels of |
new_value |
numeric value between 0 and 1. Value to which the parameter should be updated. |
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 levels should be specified.
The parameter variation specified by the function is:
P ( node
= value_node
| parents = value_parents
) = new_value
For orderp_covar
, if two or more parameters in a distribution have the same value, the order is given by the one in the respective conditional probability table. Furthermore, the parameter associated to the largest probability of the conditional probability law cannot be varied.
An object of class bn.fit
with updated probabilities.
Laskey, K. B. (1995). Sensitivity analysis for probability assessments in Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics, 25(6), 901-909.
Renooij, S. (2014). Co-variation for sensitivity analysis in Bayesian networks: Properties, consequences and alternatives. International journal of approximate reasoning, 55(4), 1022-1042.
Leonelli, M., & Riccomagno, E. (2018). A geometric characterisation of sensitivity analysis in monomial models. arXiv preprint arXiv:1901.02058.
proportional_covar(synthetic_bn, "y3", "2", c("2","1"), 0.3) uniform_covar(synthetic_bn, "y2", "1", "2", 0.3) orderp_covar(synthetic_bn, "y1", "1", NULL, 0.3)