A **Tree query** determines the resources required to calculate queries on a Bayesian network or dynamic Bayesian network
given the current evidence scenario.

A

Tree queryis generally used to evaluate the complexity of exact inference for particular queries and evidence. Approximate inference may not require the same resources as exact inference, however aTree querycan still provide useful information about the complexity of the query being performed.

Many exact inference algorithms implicitly or explicitly convert a Bayesian network or dynamic Bayesian network into a tree structure in order to perform inference (calculate queries). Some algorithms explicitly build a tree called a junction tree or a join tree, while others such as Variable Elimination are implicitly performing calculations on a tree.

A **Tree query** allows us to determine how complex this tree is in terms of a measure called **Tree width**. Tree width tells us how big
the largest computational unit is within the tree being used to calculate queries.

The results of a

Tree querywill vary depending on which nodes you are currently querying and the current evidence set. This is because algorithms can often perform optimizations when a subset of nodes are queried, and evidence often reduces the complexity of queries.