Edit Structural Coefficient

All of BayesiaLab’s structural learning algorithms use the Minimum Description Length (MDL) score to evaluate the quality of a network given its associated data set. Minimizing the MDL Score is approximately equivalent to maximizing the a posteriori of the network given the data. It is approximate because the priors of the network are approximated with a heuristic that assigns them a probability that is inversely proportional to the complexity of the network. By default, both the prior and the likelihood terms have the same weight, which is rather conservative.

There are two ways to change the weight of the heuristic used for approximating the priors.

The Overall Structural Coefficient takes values between 0 and 150. You can modify the number of particles utilized during structural learning by defining a weight for each particle that is inversely proportional to the Overall Structural Coefficient. Choosing a value of less than 1 is equivalent to increasing the number of particles and therefore increases the sensitivity of the learning algorithms, resulting in more complex structures. Choosing a value greater than 1 is equivalent to reducing the number of particles and thus decreases the sensitivity of the learning algorithms, resulting in simpler network structures. In other words, the Overall Structural Coefficient permits you to modify the “significance threshold” for detecting relationships between nodes.

Local Structural Coefficients apply because the MDL score is decomposable, which means that the score of the network is equal to the sum of the MDL scores of the nodes. A Local Structural Coefficient of less than 1 decreases the cost of adding incoming arcs to the associated node. A Local Structural Coefficient of greater than 1 increases the cost of adding incoming arcs to the associated node.