Object detection using Haar-like features is formulated as a maximum likelihood estimation. Object features are described by an arbitrary Bayesian Network (BN) of Haar-like features. We proposed variable translation techniques transform the BN into the likelihood for the object detection. The likelihood is a BN which includes a node that represents the object's position, angle and scale. The object detection can be achieved by inference for the node.