Two nodes are conditionally independent if there exists a d-seperation between them for the current givens.
Two nodes are conditionally independent if there exists a d-seperation between them for the current givens. Top nodes contain only raw probabilities, while all other nodes contain a table of conditional probabilities based upon possible combinations of parent values. FTP, name this type of belief network, widely used for probabalistic inference in AI, drawing its name from a central rule of conditional probability.
Two nodes are conditionally independent if there exists a d-seperation between them for the current givens.
Bayesian Networks or Bayes Nets