# Deviance

Deviance is formally defined as:

â

${D_B} = 2N \times \ln (2) \times \left( {{H_B}({\cal D}) - {H_C}({\cal D})} \right)$

âwhere

- â${{H_C}({\cal D})}$is the Entropy of the dataset given the complete (i.e., fully connected) network$C$. In the complete network, all nodes are directly connected to all other nodes. Therefore, the complete network$C$is an exact representation of the chain rule. As such, it does not utilize any conditional independence assumptions for representing the Joint Probability Distribution.
- â$N$is the size of the dataset.

Last modified 1mo ago