# Calculating Complexity: DL(B)

### Description Length of the Bayesian Network — DL(B)

$DL(B)$ is the number of bits required to represent a Bayesian network. We can break down this value into the sum of two components:

• $DL(G)$, which stands for the number of bits required to represent the graph G of the Bayesian network,

• $DL(P|G)$ represents the number of bits required to represent the set of probability tables P.

$DL(B) = DL(G) + DL(P|G)$

#### Calculating DL(G)

To calculate $DL(G)$, we need to determine the number of nodes and the number of their parent nodes.

DL(G) = \sum\limits_i^n {\left( {{{\log }_2}(n) + {{\log }_2}\left( {\begin{array}{*{20}{c}} n\\ {\left\| {P{a_i}} \right\|} \end{array}} \right)} \right)}

where

• n is the number of random variables (nodes): ${X_1},...,{X_n}$

• $P{a_i}$ is the set of the random variables that are parents of ${X_i}$ in graph G

• and $P{a_i}$ is the number of parents of the random variable ${X_i}$.

#### Calculating DL(P|G)

Computing $DL(P|G)$ is straightforward as it is proportional to the number of cells in all probability tables.

$DL(P|G) = \sum\limits_i^n {\left( {\prod\limits_j^{\left\| {P{a_i}} \right\|} {{S_j} \times ({S_i} - 1) \times DL(p)} } \right)}$

where

• ${{S}_{i}}$ is the number of states of the random variable ${X_i}$

• $p$ is the probability associated with the cell.

As the probability p cannot be known prior to learning the network, we use the following classical heuristic in BayesiaLab:

$DL(p) = \frac{{{{\log }_2}(N)}}{2}$

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