Marginalizing combines the probability slices corresponding to different states of the parent node by summing them up. In contrast, conditioning fixes the parent node to one specific state and examines the distribution only within that single slice.
Conditions: The model includes a parent node with multiple possible states.; We are calculating the distribution of a child node.
Marginalizing combines the probability slices corresponding to different states of the parent node by summing them up. In contrast, conditioning fixes the parent node to one specific state and examines the distribution only within that single slice.
Conditions: The model includes a parent node with multiple possible states.; We are calculating the distribution of a child node.
The graph encodes the assumption that the two child nodes (effects) are conditionally independent given the parent node. This means that once the state of the parent is known, knowing the state of one child provides no additional information about the state of the other child.
Conditions: The model is a Bayesian network with a common parent structure.; The parent node's status is specified (conditioned upon).
The graph encodes the assumption that the two child nodes (effects) are conditionally independent given the parent node. This means that once the state of the parent is known, knowing the state of one child provides no additional information about the state of the other child.
Conditions: The model is a Bayesian network with a common parent structure.; The parent node's status is specified (conditioned upon).